sentiment_analyses.ipynb

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        "id": "LON1bbnfVGHv"
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      "source": [
        "import pandas as pd\n",
        "import nltk\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZhscjM3sXy5u"
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      "source": [
        "df = pd.read_csv(\"sentiment-analysis.csv\")"
      ]
    },
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              "  Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score\n",
              "0  \"I love this product!\", Positive, Twitter, 202...                     \n",
              "1  \"The service was terrible.\", Negative, Yelp Re...                     \n",
              "2  \"This movie is amazing!\", Positive, IMDb, 2023...                     \n",
              "3  \"I'm so disappointed with their customer suppo...                     \n",
              "4  \"Just had the best meal of my life!\", Positive...                     "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"\\\"Just had the best meal of my life!\\\", Positive, TripAdvisor, 2023-06-16 08:50:59, foodie22, Paris, 0.88\",\n          \"\\\"The website layout is cluttered and confusing. Difficult to find information.\\\", Negative, Website Review, 2023-07-14 14:40:05, user2468, Sydney, 0.67\",\n          \"\\\"This book made me feel inspired. Highly recommended!\\\", Positive, Goodreads, 2023-07-02 12:34:56, bookworm789, London, 0.88\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "  Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score\n",
              "0  \"I love this product!\", Positive, Twitter, 202...                     \n",
              "1  \"The service was terrible.\", Negative, Yelp Re...                     \n",
              "2  \"This movie is amazing!\", Positive, IMDb, 2023...                     \n",
              "3  \"I'm so disappointed with their customer suppo...                     \n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca2076bf-90c1-48f9-bf18-37254b3dfdcb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"\\\"Just had the best meal of my life!\\\", Positive, TripAdvisor, 2023-06-16 08:50:59, foodie22, Paris, 0.88\",\n          \"\\\"The website layout is cluttered and confusing. Difficult to find information.\\\", Negative, Website Review, 2023-07-14 14:40:05, user2468, Sydney, 0.67\",\n          \"\\\"This book made me feel inspired. Highly recommended!\\\", Positive, Goodreads, 2023-07-02 12:34:56, bookworm789, London, 0.88\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "Y1w4h9LwdNTh",
        "outputId": "a63793d3-a5b6-4a45-e3f4-58fd76b726af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "_df_0.groupby('Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fN3eEeMpYAaJ",
        "outputId": "2e4f34bd-dc50-4fd8-acc4-e4cfc824bb4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "DdLDWwqdYJfe",
        "outputId": "fff17d9c-b675-4c61-a4bc-dd01a675c0bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
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      "execution_count": null,
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        "id": "RvdG4Xd8YR_C",
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              "                                                Text Sentiment        Source  \\\n",
              "0                             \"I love this product!\"  Positive       Twitter   \n",
              "1                        \"The service was terrible.\"  Negative  Yelp Reviews   \n",
              "2                           \"This movie is amazing!\"  Positive          IMDb   \n",
              "3  \"I'm so disappointed with their customer suppo...  Negative  Online Forum   \n",
              "4               \"Just had the best meal of my life!\"  Positive   TripAdvisor   \n",
              "\n",
              "             Date/Time      User ID     Location Confidence Score  \n",
              "0  2023-06-15 09:23:14     @user123     New York             0.85  \n",
              "1  2023-06-15 11:45:32      user456  Los Angeles             0.65  \n",
              "2  2023-06-15 14:10:22  moviefan789       London             0.92  \n",
              "3  2023-06-15 17:35:11   forumuser1      Toronto             0.78  \n",
              "4  2023-06-16 08:50:59     foodie22        Paris             0.88  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"\\\"Just had the best meal of my life!\\\"\",\n          \"\\\"The website layout is cluttered and confusing. Difficult to find information.\\\"\",\n          \"\\\"This book made me feel inspired. Highly recommended!\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Goodreads\",\n          \"Event Review\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date/Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"2023-06-16 08:50:59\",\n          \"2023-07-14 14:40:05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"foodie22\",\n          \"thrillseeker3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"0.93\",\n          \"0.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Text Sentiment        Source  \\\n",
              "0                             \"I love this product!\"  Positive       Twitter   \n",
              "1                        \"The service was terrible.\"  Negative  Yelp Reviews   \n",
              "2                           \"This movie is amazing!\"  Positive          IMDb   \n",
              "3  \"I'm so disappointed with their customer suppo...  Negative  Online Forum   \n",
              "4               \"Just had the best meal of my life!\"  Positive   TripAdvisor   \n",
              "\n",
              "             Date/Time      User ID     Location Confidence Score  \n",
              "0  2023-06-15 09:23:14     @user123     New York             0.85  \n",
              "1  2023-06-15 11:45:32      user456  Los Angeles             0.65  \n",
              "2  2023-06-15 14:10:22  moviefan789       London             0.92  \n",
              "3  2023-06-15 17:35:11   forumuser1      Toronto             0.78  \n",
              "4  2023-06-16 08:50:59     foodie22        Paris             0.88  "
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8241d07b-4623-4fa8-a0f0-0193a7a8be36 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8241d07b-4623-4fa8-a0f0-0193a7a8be36');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b8d3a89b-3891-4b11-9c1f-1c90d45c4547\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8d3a89b-3891-4b11-9c1f-1c90d45c4547')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b8d3a89b-3891-4b11-9c1f-1c90d45c4547 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"\\\"Just had the best meal of my life!\\\"\",\n          \"\\\"The website layout is cluttered and confusing. Difficult to find information.\\\"\",\n          \"\\\"This book made me feel inspired. Highly recommended!\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Goodreads\",\n          \"Event Review\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date/Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"2023-06-16 08:50:59\",\n          \"2023-07-14 14:40:05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"foodie22\",\n          \"thrillseeker3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"0.93\",\n          \"0.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "\n",
        "# Split the column and create new columns\n",
        "df[['Text', 'Sentiment', 'Source', 'Date/Time', 'User ID', 'Location', 'Confidence Score']] = df['Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score'].str.split(', ', expand=True)\n",
        "\n",
        "# Drop the original combined column if you don't need it anymore\n",
        "df = df.drop('Text, Sentiment, Source, Date/Time, User ID, Location, Confidence Score', axis=1)\n",
        "\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8euVqDfLZBq2",
        "outputId": "d2882d48-2703-46ea-c4e9-0709a13517cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "JM5ldlX0ZG7A",
        "outputId": "b94741fb-a261-4b8b-85b2-1f53e61bfe6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Text Sentiment        Source  \\\n",
              "0                              I love this product!  Positive       Twitter   \n",
              "1                         The service was terrible.  Negative  Yelp Reviews   \n",
              "2                            This movie is amazing!  Positive          IMDb   \n",
              "3  I'm so disappointed with their customer support.  Negative  Online Forum   \n",
              "4                Just had the best meal of my life!  Positive   TripAdvisor   \n",
              "\n",
              "             Date/Time      User ID     Location Confidence Score  \n",
              "0  2023-06-15 09:23:14     @user123     New York             0.85  \n",
              "1  2023-06-15 11:45:32      user456  Los Angeles             0.65  \n",
              "2  2023-06-15 14:10:22  moviefan789       London             0.92  \n",
              "3  2023-06-15 17:35:11   forumuser1      Toronto             0.78  \n",
              "4  2023-06-16 08:50:59     foodie22        Paris             0.88  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d77b898-151e-4c51-a3d3-8e05f0b4eeb8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Source</th>\n",
              "      <th>Date/Time</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Location</th>\n",
              "      <th>Confidence Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I love this product!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>2023-06-15 09:23:14</td>\n",
              "      <td>@user123</td>\n",
              "      <td>New York</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The service was terrible.</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Yelp Reviews</td>\n",
              "      <td>2023-06-15 11:45:32</td>\n",
              "      <td>user456</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>This movie is amazing!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>IMDb</td>\n",
              "      <td>2023-06-15 14:10:22</td>\n",
              "      <td>moviefan789</td>\n",
              "      <td>London</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I'm so disappointed with their customer support.</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Online Forum</td>\n",
              "      <td>2023-06-15 17:35:11</td>\n",
              "      <td>forumuser1</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Just had the best meal of my life!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>TripAdvisor</td>\n",
              "      <td>2023-06-16 08:50:59</td>\n",
              "      <td>foodie22</td>\n",
              "      <td>Paris</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d77b898-151e-4c51-a3d3-8e05f0b4eeb8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d77b898-151e-4c51-a3d3-8e05f0b4eeb8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d77b898-151e-4c51-a3d3-8e05f0b4eeb8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2c7842fa-f4ee-4cd3-bd1e-de8548ae476c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2c7842fa-f4ee-4cd3-bd1e-de8548ae476c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2c7842fa-f4ee-4cd3-bd1e-de8548ae476c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"Just had the best meal of my life!\",\n          \"The website layout is cluttered and confusing. Difficult to find information.\",\n          \"This book made me feel inspired. Highly recommended!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Goodreads\",\n          \"Event Review\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date/Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"2023-06-16 08:50:59\",\n          \"2023-07-14 14:40:05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"foodie22\",\n          \"thrillseeker3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"0.93\",\n          \"0.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Text Sentiment        Source  \\\n",
              "0                              I love this product!  Positive       Twitter   \n",
              "1                         The service was terrible.  Negative  Yelp Reviews   \n",
              "2                            This movie is amazing!  Positive          IMDb   \n",
              "3  I'm so disappointed with their customer support.  Negative  Online Forum   \n",
              "4                Just had the best meal of my life!  Positive   TripAdvisor   \n",
              "\n",
              "             Date/Time      User ID     Location Confidence Score  \n",
              "0  2023-06-15 09:23:14     @user123     New York             0.85  \n",
              "1  2023-06-15 11:45:32      user456  Los Angeles             0.65  \n",
              "2  2023-06-15 14:10:22  moviefan789       London             0.92  \n",
              "3  2023-06-15 17:35:11   forumuser1      Toronto             0.78  \n",
              "4  2023-06-16 08:50:59     foodie22        Paris             0.88  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb6f955d-897b-4d3d-bb03-f46b3385f1d3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Source</th>\n",
              "      <th>Date/Time</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Location</th>\n",
              "      <th>Confidence Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I love this product!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>2023-06-15 09:23:14</td>\n",
              "      <td>@user123</td>\n",
              "      <td>New York</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The service was terrible.</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Yelp Reviews</td>\n",
              "      <td>2023-06-15 11:45:32</td>\n",
              "      <td>user456</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>This movie is amazing!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>IMDb</td>\n",
              "      <td>2023-06-15 14:10:22</td>\n",
              "      <td>moviefan789</td>\n",
              "      <td>London</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I'm so disappointed with their customer support.</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Online Forum</td>\n",
              "      <td>2023-06-15 17:35:11</td>\n",
              "      <td>forumuser1</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Just had the best meal of my life!</td>\n",
              "      <td>Positive</td>\n",
              "      <td>TripAdvisor</td>\n",
              "      <td>2023-06-16 08:50:59</td>\n",
              "      <td>foodie22</td>\n",
              "      <td>Paris</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb6f955d-897b-4d3d-bb03-f46b3385f1d3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cb6f955d-897b-4d3d-bb03-f46b3385f1d3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb6f955d-897b-4d3d-bb03-f46b3385f1d3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-181c511e-0ada-4081-a8ea-3c0e8485991c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-181c511e-0ada-4081-a8ea-3c0e8485991c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-181c511e-0ada-4081-a8ea-3c0e8485991c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"Just had the best meal of my life!\",\n          \"The website layout is cluttered and confusing. Difficult to find information.\",\n          \"This book made me feel inspired. Highly recommended!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Goodreads\",\n          \"Event Review\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date/Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"2023-06-16 08:50:59\",\n          \"2023-07-14 14:40:05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"foodie22\",\n          \"thrillseeker3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confidence Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"0.93\",\n          \"0.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "df['Text'] = df['Text'].str.replace('\"', '')\n",
        "\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "2DgwkhfMbSIT",
        "outputId": "7960161c-240e-4994-b41d-0d66c10cb70f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title Sentiment\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "df.groupby('Sentiment').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
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        "id": "O_10hbm_ZVY8",
        "outputId": "430c1110-890e-4f2f-b3b4-c97a384e1e59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                2\n",
              "Sentiment           2\n",
              "Source              2\n",
              "Date/Time           2\n",
              "User ID             2\n",
              "Location            2\n",
              "Confidence Score    2\n",
              "dtype: int64"
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 64
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "Confidence Score    2\n",
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      "source": [
        "df.isnull().sum()"
      ]
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    {
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        "id": "MbHImT8bgL1M",
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      "outputs": [
        {
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              "                                               Text Sentiment\n",
              "0                              I love this product!  Positive\n",
              "1                         The service was terrible.  Negative\n",
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              "        document.querySelector('#df-6a1bdf97-e653-4f36-8637-0b5b53a6efc8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6a1bdf97-e653-4f36-8637-0b5b53a6efc8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef275099-c18e-4459-abe2-80e755f95018 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"Just had the best meal of my life!\",\n          \"The website layout is cluttered and confusing. Difficult to find information.\",\n          \"This book made me feel inspired. Highly recommended!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Text Sentiment\n",
              "0                              I love this product!  Positive\n",
              "1                         The service was terrible.  Negative\n",
              "2                            This movie is amazing!  Positive\n",
              "3  I'm so disappointed with their customer support.  Negative\n",
              "4                Just had the best meal of my life!  Positive"
            ],
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              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-643763e7-68f4-4ec7-9c29-f0e7fb6b44f5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-643763e7-68f4-4ec7-9c29-f0e7fb6b44f5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-75b81097-add5-451c-99e7-916c9b09102a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75b81097-add5-451c-99e7-916c9b09102a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-75b81097-add5-451c-99e7-916c9b09102a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"Just had the best meal of my life!\",\n          \"The website layout is cluttered and confusing. Difficult to find information.\",\n          \"This book made me feel inspired. Highly recommended!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "# create a dataframe with the columns Text\tSentiment only\n",
        "\n",
        "new_df = df[['Text', 'Sentiment']]\n",
        "new_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "yQWy86Qrgb1o",
        "outputId": "3293c844-d8cc-4db8-c2b6-79dee07c61cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text         object\n",
              "Sentiment    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text         object\n",
              "Sentiment    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "new_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHM_Xi_IgnII",
        "outputId": "d73cf34c-e2a9-45ca-b737-8dca139183b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Positive', 'Negative', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Positive', 'Negative', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "new_df[\"Sentiment\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVgbprA7gyzM",
        "outputId": "cff289fa-598e-4da8-cde3-a5ffec265489"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "6SWhhOIWhB1z",
        "outputId": "b87436e8-acaf-46a0-e2a0-080dfc266354"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text         2\n",
              "Sentiment    2\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text         2\n",
              "Sentiment    2\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "new_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "328CKh2ehH2L",
        "outputId": "9ad9e9a2-804c-4787-e4db-7a3c4ec9949a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-ca20e18b38b5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  new_df.dropna(inplace=True)\n",
            "<ipython-input-93-ca20e18b38b5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  new_df.dropna(inplace=True)\n"
          ]
        }
      ],
      "source": [
        "new_df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_Q3spAahNe9",
        "outputId": "d0112001-6797-4967-9a20-b00b05daa0d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 961
        },
        "id": "Mnw4L7Q-hZ-3",
        "outputId": "d60ba572-4855-4b51-8ffa-a97a817becbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# checking for outliers\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.boxplot(x='Sentiment', y=new_df['Text'].apply(len), data=new_df)\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 961
        },
        "id": "FtD6HTbzh4fd",
        "outputId": "911bdaaf-fb8e-4fd6-9945-cf5c7c7e4943"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: drop the outliers and draw the diagram again\n",
        "\n",
        "# Calculate IQR for text length\n",
        "Q1 = new_df['Text'].apply(len).quantile(0.25)\n",
        "Q3 = new_df['Text'].apply(len).quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Define bounds for outliers\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Filter out outliers\n",
        "new_df = new_df[(new_df['Text'].apply(len) >= lower_bound) & (new_df['Text'].apply(len) <= upper_bound)]\n",
        "\n",
        "# Redraw the boxplot\n",
        "sns.boxplot(x='Sentiment', y=new_df['Text'].apply(len), data=new_df)\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQSdgJqBiGaZ",
        "outputId": "131c5d78-cd71-42ff-a266-b3ae20b22c66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(92, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(92, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beb2T52aiRgI",
        "outputId": "14128790-1379-4f2d-811f-ef03f11d76ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 19\n",
            "Number of duplicate rows: 19\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Check for duplicate rows\n",
        "duplicate_rows = new_df[new_df.duplicated()]\n",
        "print(\"Number of duplicate rows:\", duplicate_rows.shape[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: draw a barchat diagram to represent the duplicates\n",
        "\n",
        "# Count the duplicate rows\n",
        "duplicate_counts = duplicate_rows.groupby(['Text', 'Sentiment']).size().reset_index(name='Count')\n",
        "\n",
        "# Plot the bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Count', y='Text', hue='Sentiment', data=duplicate_counts)\n",
        "plt.title('Count of Duplicate Rows')\n",
        "plt.xlabel('Number of Duplicates')\n",
        "plt.ylabel('Text')\n",
        "plt.legend(title='Sentiment')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bFnm75rwrauk",
        "outputId": "df2ef78a-0ee9-4c11-aaef-08120dfb074e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z6bMr7lMi8pB"
      },
      "outputs": [],
      "source": [
        "# To remove duplicates:\n",
        "new_df.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fe8oFrczjEfG",
        "outputId": "ce883b85-8bd5-4355-819d-89aadfc0debb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "reZ2deI-ZbSf",
        "outputId": "b71a5dba-b0f9-4ff4-9a25-0688498279c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                object\n",
              "Sentiment           object\n",
              "Source              object\n",
              "Date/Time           object\n",
              "User ID             object\n",
              "Location            object\n",
              "Confidence Score    object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confidence Score</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYMBhLyXavQB",
        "outputId": "2b2769c0-ae75-4669-8337-1e719034e5d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New York', 'Los Angeles', 'London', 'Toronto', 'Paris',\n",
              "       'San Francisco', 'Berlin', 'Sydney', 'Mumbai', 'Orlando',\n",
              "       'Chicago', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "df[\"Location\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "tWT4LftTbsOi",
        "outputId": "d9b402e3-4491-4392-c499-639c4adf8ec3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                  object\n",
              "Sentiment             object\n",
              "Source              category\n",
              "Date/Time             object\n",
              "User ID               object\n",
              "Location            category\n",
              "Confidence Score      object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confidence Score</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "# convert Location, Source,  to categorical data type\n",
        "\n",
        "df['Location'] = df['Location'].astype('category')\n",
        "df['Source'] = df['Source'].astype('category')\n",
        "df.dtypes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "iNgom_y1b-8v",
        "outputId": "6f42f4be-c53d-45f2-cab9-b59ea092a634"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                  object\n",
              "Sentiment             object\n",
              "Source              category\n",
              "Date/Time             object\n",
              "User ID               object\n",
              "Location            category\n",
              "Confidence Score     float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confidence Score</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "# convert Confidence Score to float datatype\n",
        "\n",
        "df['Confidence Score'] = df['Confidence Score'].astype(float)\n",
        "df.dtypes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "elXDVqkMcJhZ",
        "outputId": "7e62d1c6-7baf-467d-d6c3-a9859186972a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                        object\n",
              "Sentiment                   object\n",
              "Source                    category\n",
              "Date/Time           datetime64[ns]\n",
              "User ID                     object\n",
              "Location                  category\n",
              "Confidence Score           float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confidence Score</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "# convert Date/Time to time data type\n",
        "\n",
        "df['Date/Time'] = pd.to_datetime(df['Date/Time'])\n",
        "df.dtypes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C7DuzXbucWhx"
      },
      "outputs": [],
      "source": [
        "#convert Sentiment to category datatpe\n",
        "df[\"Sentiment\"] = df[\"Sentiment\"].astype(\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "BI5Ht5UMcnv1",
        "outputId": "c7bb2f35-a78e-410a-f8f3-f5d5f0aa4aac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text                        object\n",
              "Sentiment                 category\n",
              "Source                    category\n",
              "Date/Time           datetime64[ns]\n",
              "User ID                     object\n",
              "Location                  category\n",
              "Confidence Score           float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Text</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sentiment</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confidence Score</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssqkGpgvd4b5",
        "outputId": "8d4043be-0757-4436-b2f9-ac0c8eb0252a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "# Download stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_9YtUfy5eXI-"
      },
      "outputs": [],
      "source": [
        "# Initialize CountVectorizer\n",
        "vectorizer = CountVectorizer(stop_words=stopwords.words('english'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r7nc5Ai7eaKc"
      },
      "outputs": [],
      "source": [
        "# Fit and transform the review text\n",
        "X = vectorizer.fit_transform(new_df['Text'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMAvheVwelyK",
        "outputId": "1ec4ef43-5aac-4874-af9c-1f6bbf3d2ee9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features: ['absolutely' 'addicted' 'adrenaline' 'air' 'album' 'always' 'amazing'\n",
            " 'arrived' 'art' 'artist' 'avoid' 'away' 'awe' 'awful' 'back' 'band'\n",
            " 'beautifully' 'best' 'beyond' 'blowing' 'blown' 'book' 'bored' 'breath'\n",
            " 'breathtaking' 'broke' 'cafe' 'calming' 'captivated' 'captivating' 'care'\n",
            " 'chat' 'cluttered' 'coaster' 'concert' 'confusing' 'contagious' 'costs'\n",
            " 'cruise' 'customer' 'customers' 'damaged' 'delayed' 'delicious'\n",
            " 'delivery' 'design' 'designed' 'difficult' 'disappointed' 'disappointing'\n",
            " 'easy' 'electrifying' 'ending' 'energizing' 'energy' 'enough' 'ever'\n",
            " 'every' 'exceeded' 'excellent' 'exceptional' 'excitement' 'exhilarating'\n",
            " 'expectations' 'experience' 'extremely' 'fantastic' 'favorite' 'feel'\n",
            " 'fell' 'find' 'finest' 'finish' 'flat' 'flight' 'follow' 'food' 'found'\n",
            " 'fresh' 'friendly' 'frustrating' 'frustratingly' 'fun' 'game' 'get' 'go'\n",
            " 'going' 'good' 'great' 'hard' 'headphones' 'heart' 'helpful' 'highly'\n",
            " 'hooked' 'hotel' 'impressed' 'improvement' 'incredible' 'information'\n",
            " 'inspired' 'intuitive' 'issue' 'kept' 'knows' 'late' 'layout' 'left'\n",
            " 'life' 'listening' 'live' 'loading' 'love' 'loved' 'luxury' 'made' 'many'\n",
            " 'masterpiece' 'meal' 'mediocre' 'mind' 'moment' 'mood' 'motivating'\n",
            " 'movie' 'much' 'music' 'must' 'navigate' 'navigating' 'navigation'\n",
            " 'needed' 'needs' 'never' 'new' 'nostalgic' 'notch' 'nothing' 'ordered'\n",
            " 'outstanding' 'packaging' 'paintings' 'park' 'perfect' 'performance'\n",
            " 'playlist' 'plot' 'poor' 'poorly' 'pounding' 'price' 'product' 'provided'\n",
            " 'provoking' 'purchased' 'pure' 'put' 'puts' 'quality' 'questions' 'quick'\n",
            " 'quickly' 'read' 'received' 'recommend' 'recommended' 'relaxation'\n",
            " 'reminds' 'resolution' 'resolved' 'respond' 'restaurant' 'ride' 'roller'\n",
            " 'rude' 'rush' 'service' 'ship' 'show' 'sleek' 'slow' 'smooth' 'song'\n",
            " 'soothing' 'sound' 'special' 'speed' 'staff' 'start' 'stay' 'stop'\n",
            " 'store' 'subpar' 'support' 'talent' 'tasteless' 'tears' 'technical'\n",
            " 'terrible' 'theme' 'thought' 'thrilling' 'times' 'top' 'track' 'truly'\n",
            " 'try' 'tv' 'unacceptable' 'unanswered' 'unforgettable' 'unique'\n",
            " 'unprofessional' 'unresponsive' 'user' 'uses' 'vacation' 'wait' 'website'\n",
            " 'week' 'went' 'within' 'work' 'workouts' 'worst' 'worth' 'written']\n"
          ]
        }
      ],
      "source": [
        "# Display feature names\n",
        "print(\"Features:\", vectorizer.get_feature_names_out())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtVrdo1EfCG-",
        "outputId": "3d2d51bb-0547-422a-e2ed-7ad09c90e80f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training samples: 54, Testing samples: 19\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, new_df['Sentiment'], test_size=0.25, random_state=42)\n",
        "print(f\"Training samples: {X_train.shape[0]}, Testing samples: {X_test.shape[0]}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "XoHNSnx7fJqN",
        "outputId": "047fc616-fff3-493c-ac0d-f5d113ed6c7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Initialize and train the classifier\n",
        "model = MultinomialNB()\n",
        "model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwUdcPtOf1r8",
        "outputId": "3090c036-33f6-496a-8375-0def027247e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 84.21%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.77      1.00      0.87        10\n",
            "    Positive       1.00      0.67      0.80         9\n",
            "\n",
            "    accuracy                           0.84        19\n",
            "   macro avg       0.88      0.83      0.83        19\n",
            "weighted avg       0.88      0.84      0.84        19\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Predict sentiment for the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "# Display classification report\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data from the classification report\n",
        "metrics = ['Precision', 'Recall', 'F1-Score']\n",
        "negative_scores = [0.77, 1.00, 0.87]\n",
        "positive_scores = [1.00, 0.67, 0.80]\n",
        "\n",
        "# Set width of bars\n",
        "bar_width = 0.35\n",
        "\n",
        "# Set position of bars on X axis\n",
        "r1 = range(len(metrics))\n",
        "r2 = [x + bar_width for x in r1]\n",
        "\n",
        "# Plot bars\n",
        "plt.bar(r1, negative_scores, color='blue', width=bar_width, edgecolor='white', label='Negative')\n",
        "plt.bar(r2, positive_scores, color='green', width=bar_width, edgecolor='white', label='Positive')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "plt.xlabel('Metrics')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Classification Report Metrics')\n",
        "plt.xticks([r + bar_width/2 for r in range(len(metrics))], metrics)\n",
        "plt.legend()\n",
        "\n",
        "# Display the chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EdVsuNst6H0g",
        "outputId": "c30fdb0b-21ae-43bd-a699-cf160e381cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "g69GUmOs0Aur",
        "outputId": "2f34fa9b-0e27-4097-863f-5f90ba7dd95d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=model.classes_, yticklabels=model.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Experiment with other algorithms like Support Vector Machines (SVM) or Decision Trees.\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "b_kQtZwx7Z6f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize SVM classifier\n",
        "svm_model = SVC()\n",
        "svm_model.fit(X_train, y_train)\n",
        "y_pred_svm = svm_model.predict(X_test)\n",
        "print(\"SVM Accuracy:\", accuracy_score(y_test, y_pred_svm))\n",
        "\n",
        "# Display classification report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53JkkAx79-t_",
        "outputId": "901b0ab7-d8bc-4f3a-8fd4-be93a6050838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy: 0.7368421052631579\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.77      1.00      0.87        10\n",
            "    Positive       1.00      0.67      0.80         9\n",
            "\n",
            "    accuracy                           0.84        19\n",
            "   macro avg       0.88      0.83      0.83        19\n",
            "weighted avg       0.88      0.84      0.84        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data from the classification report for SVM\n",
        "metrics = ['Precision', 'Recall', 'F1-Score']\n",
        "negative_scores_svm = [0.77, 1.00, 0.87]\n",
        "positive_scores_svm = [1.00, 0.67, 0.80]\n",
        "\n",
        "# Set width of bars\n",
        "bar_width = 0.35\n",
        "\n",
        "# Set position of bars on X axis\n",
        "r1 = range(len(metrics))\n",
        "r2 = [x + bar_width for x in r1]\n",
        "\n",
        "# Plot bars\n",
        "plt.bar(r1, negative_scores_svm, color='blue', width=bar_width, edgecolor='white', label='Negative')\n",
        "plt.bar(r2, positive_scores_svm, color='green', width=bar_width, edgecolor='white', label='Positive')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "plt.xlabel('Metrics')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('SVM Classification Report Metrics')\n",
        "plt.xticks([r + bar_width/2 for r in range(len(metrics))], metrics)\n",
        "plt.legend()\n",
        "\n",
        "# Display the chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "9CXKWz7U-gvH",
        "outputId": "ec6eec23-ca5e-4ee0-cd12-32ca57be8317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Decision Tree classifier\n",
        "tree_model = DecisionTreeClassifier()\n",
        "tree_model.fit(X_train, y_train)\n",
        "y_pred_tree = tree_model.predict(X_test)\n",
        "print(\"Decision Tree Accuracy:\", accuracy_score(y_test, y_pred_tree))\n",
        "\n",
        "# Display classification report\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFMNvvBB-BCh",
        "outputId": "61c24b9a-58fd-4015-de0b-27c6ada0aa24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.631578947368421\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.77      1.00      0.87        10\n",
            "    Positive       1.00      0.67      0.80         9\n",
            "\n",
            "    accuracy                           0.84        19\n",
            "   macro avg       0.88      0.83      0.83        19\n",
            "weighted avg       0.88      0.84      0.84        19\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data from the classification report for Decision Tree\n",
        "metrics = ['Precision', 'Recall', 'F1-Score']\n",
        "negative_scores_tree = [0.77, 1.00, 0.87]\n",
        "positive_scores_tree = [1.00, 0.67, 0.80]\n",
        "\n",
        "# Set width of bars\n",
        "bar_width = 0.35\n",
        "\n",
        "# Set position of bars on X axis\n",
        "r1 = range(len(metrics))\n",
        "r2 = [x + bar_width for x in r1]\n",
        "\n",
        "# Plot bars\n",
        "plt.bar(r1, negative_scores_tree, color='blue', width=bar_width, edgecolor='white', label='Negative')\n",
        "plt.bar(r2, positive_scores_tree, color='green', width=bar_width, edgecolor='white', label='Positive')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "plt.xlabel('Metrics')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Decision Tree Classification Report Metrics')\n",
        "plt.xticks([r + bar_width/2 for r in range(len(metrics))], metrics)\n",
        "plt.legend()\n",
        "\n",
        "# Display the chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EMefw9Nd-CWI",
        "outputId": "c16002bc-4067-44d0-de3c-6cd075add2da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CceB77bpBskW"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOq9qJ06WJ4LM3HC2d9Eb+c",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}