getting_started_(1).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArunaAcharya/Data-Science/blob/main/getting_started_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wl29uJi_qJMa"
      },
      "source": [
        "# Task 1\n",
        "\n",
        "---\n",
        "\n",
        "## Web scraping and analysis\n",
        "\n",
        "This Jupyter notebook includes some code to get you started with web scraping. We will use a package called `BeautifulSoup` to collect the data from the web. Once you've collected your data and saved it into a local `.csv` file you should start with your analysis.\n",
        "\n",
        "### Scraping data from Skytrax\n",
        "\n",
        "If you visit [https://www.airlinequality.com] you can see that there is a lot of data there. For this task, we are only interested in reviews related to British Airways and the Airline itself.\n",
        "\n",
        "If you navigate to this link: [https://www.airlinequality.com/airline-reviews/british-airways] you will see this data. Now, we can use `Python` and `BeautifulSoup` to collect all the links to the reviews and then to collect the text data on each of the individual review links."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "id": "c0MsS1oZqJMc"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import nltk\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "import ssl\n",
        "\n",
        "try:\n",
        "    _create_unverified_https_context = ssl._create_unverified_context\n",
        "except AttributeError:\n",
        "    pass\n",
        "else:\n",
        "    ssl._create_default_https_context = _create_unverified_https_context\n",
        "\n",
        "nltk.download('all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpZDy7ifwJW-",
        "outputId": "d09b61eb-37e0-49ec-be07-80c0d2511463"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "NHdJs06TqJMc"
      },
      "outputs": [],
      "source": [
        "base_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\n",
        "pages = 10\n",
        "page_size = 100\n",
        "\n",
        "reviews = []\n",
        "\n",
        "# for i in range(1, pages + 1):\n",
        "for i in range(1, pages + 1):\n",
        "\n",
        "\n",
        "    # Create URL to collect links from paginated data\n",
        "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
        "\n",
        "    # Collect HTML data from this page\n",
        "    response = requests.get(url)\n",
        "\n",
        "    # Parse content\n",
        "    content = response.content\n",
        "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
        "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
        "        reviews.append(para.get_text())\n",
        "        \n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "                                             reviews\n",
              "1    Vancouver to Delhi via London. We were booke...\n",
              "2    Old A320 with narrow pitch. Flight perfectly...\n",
              "3    Another BA Shambles. Started off well with e...\n",
              "4    BA cancelled my flight home to Heathrow on D...\n",
              "5    BA cancelled my flight home, the last flight..."
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      ],
      "source": [
        "df = pd.DataFrame()\n",
        "\n",
        "df[\"reviews\"] = reviews\n",
        "df['reviews']= df['reviews'].str.lstrip(\"✅ Trip Not Verified \")\n",
        "df['reviews']= df['reviews'].str.lstrip(\"|\")\n",
        "df.index += 1 \n",
        "\n",
        "df.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
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        "id": "5NQyWGb1qJMe",
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              "       id                                            reviews\n",
              "0       1    Vancouver to Delhi via London. We were booke...\n",
              "1       2    Old A320 with narrow pitch. Flight perfectly...\n",
              "2       3    Another BA Shambles. Started off well with e...\n",
              "3       4    BA cancelled my flight home to Heathrow on D...\n",
              "4       5    BA cancelled my flight home, the last flight...\n",
              "..    ...                                                ...\n",
              "995   996    Tampa to Gatwick. I am a disabled traveller ...\n",
              "996   997    Heathrow to Keflavik. I had booked economy, ...\n",
              "997   998    London to Muscat first class. First time try...\n",
              "998   999    My family and I travelled from Cape Town to ...\n",
              "999  1000    Gatwick to Madeira. The flight left on time ...\n",
              "\n",
              "[1000 rows x 2 columns]"
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              "      <th>...</th>\n",
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              "      <th>995</th>\n",
              "      <td>996</td>\n",
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              "      <th>996</th>\n",
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              "      <td>1000</td>\n",
              "      <td>Gatwick to Madeira. The flight left on time ...</td>\n",
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              "<p>1000 rows × 2 columns</p>\n",
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      "source": [
        "df.reset_index().rename(columns={'index':'id'})"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df['id']= df.index\n",
        "df.head()\n"
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              "                                             reviews  id\n",
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              "            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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ix5W9BtXqJMe"
      },
      "source": [
        "Congratulations! Now you have your dataset for this task! The loops above collected 1000 reviews by iterating through the paginated pages on the website. However, if you want to collect more data, try increasing the number of pages!\n",
        "\n",
        " The next thing that you should do is clean this data to remove any unnecessary text from each of the rows. For example, \"✅ Trip Verified\" can be removed from each row if it exists, as it's not relevant to what we want to investigate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "_pLFoFr1qJMe"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "feH-MCzyr0mq"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "NLTK\n"
      ],
      "metadata": {
        "id": "ktSBK-_sr46T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example= df['reviews'][10]\n",
        "print(example)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGBefsxNr6ie",
        "outputId": "d64fc33b-edda-4fb7-fe54-fd56f266fb5f"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Seat horribly narrow; 3-4-3 on a 777. Thankfully the flight was only a quarter full so was able to stretch out. Food and beverage service was good and it was great having a proper beer not a cup of froth that overflows! Cabin crew very good. Booked as a BA ticket that included travel on Qatar Airways as well.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens= nltk.wordpunct_tokenize(example)\n",
        "tokens[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqOC7QkisDvT",
        "outputId": "bf8fd1be-5871-4dae-cbda-c67f97057f44"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Seat', 'horribly', 'narrow', ';', '3', '-', '4', '-', '3', 'on']"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagged= nltk.pos_tag(tokens)\n",
        "tagged[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRP4o3BjsI0Z",
        "outputId": "da2936e0-196d-4d0e-8997-8998fcf85dbf"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Seat', 'NN'),\n",
              " ('horribly', 'RB'),\n",
              " ('narrow', 'JJ'),\n",
              " (';', ':'),\n",
              " ('3', 'CD'),\n",
              " ('-', ':'),\n",
              " ('4', 'CD'),\n",
              " ('-', ':'),\n",
              " ('3', 'CD'),\n",
              " ('on', 'IN')]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "entities= nltk.chunk.ne_chunk(tagged)\n",
        "entities.pprint()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-0FBwZssldj",
        "outputId": "f10d3633-8ff9-4c32-9450-6807e4997b74"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(S\n",
            "  (GPE Seat/NN)\n",
            "  horribly/RB\n",
            "  narrow/JJ\n",
            "  ;/:\n",
            "  3/CD\n",
            "  -/:\n",
            "  4/CD\n",
            "  -/:\n",
            "  3/CD\n",
            "  on/IN\n",
            "  a/DT\n",
            "  777/CD\n",
            "  ./.\n",
            "  Thankfully/RB\n",
            "  the/DT\n",
            "  flight/NN\n",
            "  was/VBD\n",
            "  only/RB\n",
            "  a/DT\n",
            "  quarter/NN\n",
            "  full/JJ\n",
            "  so/RB\n",
            "  was/VBD\n",
            "  able/JJ\n",
            "  to/TO\n",
            "  stretch/VB\n",
            "  out/RP\n",
            "  ./.\n",
            "  (PERSON Food/NN)\n",
            "  and/CC\n",
            "  beverage/NN\n",
            "  service/NN\n",
            "  was/VBD\n",
            "  good/JJ\n",
            "  and/CC\n",
            "  it/PRP\n",
            "  was/VBD\n",
            "  great/JJ\n",
            "  having/VBG\n",
            "  a/DT\n",
            "  proper/JJ\n",
            "  beer/NN\n",
            "  not/RB\n",
            "  a/DT\n",
            "  cup/NN\n",
            "  of/IN\n",
            "  froth/NN\n",
            "  that/WDT\n",
            "  overflows/VBZ\n",
            "  !/.\n",
            "  (PERSON Cabin/NNP)\n",
            "  crew/VBD\n",
            "  very/RB\n",
            "  good/JJ\n",
            "  ./.\n",
            "  Booked/VBN\n",
            "  as/IN\n",
            "  a/DT\n",
            "  BA/NNP\n",
            "  ticket/NN\n",
            "  that/WDT\n",
            "  included/VBD\n",
            "  travel/NN\n",
            "  on/IN\n",
            "  (ORGANIZATION Qatar/NNP Airways/NNP)\n",
            "  as/RB\n",
            "  well/RB\n",
            "  ./.)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "from tqdm.notebook import tqdm"
      ],
      "metadata": {
        "id": "L8zGVkkjxS_k"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sia = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "-fsnlu3CxcVE"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sia.polarity_scores(example)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20cAkuGFxeov",
        "outputId": "be39cdfb-124a-4ec5-9bb9-31fe944409c3"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'neg': 0.051, 'neu': 0.722, 'pos': 0.227, 'compound': 0.8932}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "2B3VuhTKC4kN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run the polarity score on the entire dataset\n"
      ],
      "metadata": {
        "id": "AuabLWMhDM1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res={}\n",
        "for i, row in tqdm(df.iterrows(), total= len(df)):\n",
        "  text= row['reviews'] \n",
        "  myid=row['id']\n",
        "  res[myid]= sia.polarity_scores(text)\n",
        "\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "vEvUxouvxiq8",
        "outputId": "e4a1bfaf-f234-4714-f8c3-85c6377411a0"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "vaders= pd.DataFrame(res).T\n",
        "vaders.reset_index().rename(columns={'index':'id'})\n",
        "vaders['id']= vaders.index\n"
      ],
      "metadata": {
        "id": "5SKklqYOx4Co"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df.head()\n"
      ],
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      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             reviews  id\n",
              "1    Vancouver to Delhi via London. We were booke...   1\n",
              "2    Old A320 with narrow pitch. Flight perfectly...   2\n",
              "3    Another BA Shambles. Started off well with e...   3\n",
              "4    BA cancelled my flight home to Heathrow on D...   4\n",
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            "text/plain": [
              "   neg  neu  pos  compound  id\n",
              "1  0.1  0.8  0.0      -1.0   1\n",
              "2  0.0  0.7  0.3       0.9   2\n",
              "3  0.1  0.7  0.2       0.9   3\n",
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          },
          "metadata": {},
          "execution_count": 254
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df= pd.merge(df, vaders, how= \"left\", right_on= 'id', left_on=\"id\")"
      ],
      "metadata": {
        "id": "zJcRsrCxOV9v"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.head()"
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        "id": "z3t0HUG2OoUI",
        "outputId": "244ff2e5-5f9e-42b3-c031-cd94f0baa6b7"
      },
      "execution_count": 275,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                             reviews  id  neg  neu  pos  \\\n",
              "0    Vancouver to Delhi via London. We were booke...   1  0.1  0.8  0.0   \n",
              "1    Old A320 with narrow pitch. Flight perfectly...   2  0.0  0.7  0.3   \n",
              "2    Another BA Shambles. Started off well with e...   3  0.1  0.7  0.2   \n",
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              "4    BA cancelled my flight home, the last flight...   5  0.1  0.8  0.1   \n",
              "\n",
              "   compound  \n",
              "0      -1.0  \n",
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        "ax= sns.barplot(data= merged_df, x= 'id', y= 'compound')\n",
        "ax.set_title(\"Compound score\")\n",
        "plt.show()"
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      "execution_count": 258,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CG1B0Uu4SV6D"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xciQxQHKVcih"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs= plt.subplots(1, 3, figsize= (15, 5))\n",
        "sns.barplot(data= merged_df, x= 'id', y='pos', ax= axs[0])\n",
        "sns.barplot(data= merged_df, x= 'id', y='neu', ax= axs[1])\n",
        "sns.barplot(data= merged_df, x= 'id', y='neg', ax= axs[2])\n",
        "axs[0].set_title('Positive')\n",
        "axs[1].set_title('Neutral')\n",
        "axs[2].set_title('Negative')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "Ymx6brDxVnhv",
        "outputId": "6df1667c-038e-48ab-f2d6-3bb0cc519d25"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['status']='neutral'"
      ],
      "metadata": {
        "id": "3nrUbdPKZVih"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3q-jEeokdy3g",
        "outputId": "ce764497-32e1-4e40-e650-72d2865775f9"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             reviews  id  neg  neu  pos  \\\n",
              "0    Vancouver to Delhi via London. We were booke...   1  0.1  0.8  0.0   \n",
              "1    Old A320 with narrow pitch. Flight perfectly...   2  0.0  0.7  0.3   \n",
              "2    Another BA Shambles. Started off well with e...   3  0.1  0.7  0.2   \n",
              "3    BA cancelled my flight home to Heathrow on D...   4  0.2  0.8  0.0   \n",
              "4    BA cancelled my flight home, the last flight...   5  0.1  0.8  0.1   \n",
              "\n",
              "   compound   status  \n",
              "0      -1.0  neutral  \n",
              "1       0.9  neutral  \n",
              "2       0.9  neutral  \n",
              "3      -1.0  neutral  \n",
              "4      -0.8  neutral  "
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              "      <th>reviews</th>\n",
              "      <th>id</th>\n",
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              "      <th>neu</th>\n",
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              "      <th>0</th>\n",
              "      <td>Vancouver to Delhi via London. We were booke...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Old A320 with narrow pitch. Flight perfectly...</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Another BA Shambles. Started off well with e...</td>\n",
              "      <td>3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BA cancelled my flight home to Heathrow on D...</td>\n",
              "      <td>4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BA cancelled my flight home, the last flight...</td>\n",
              "      <td>5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8d00da0-192f-4d29-90cd-d1a6615f7ea5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8d00da0-192f-4d29-90cd-d1a6615f7ea5 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-e8d00da0-192f-4d29-90cd-d1a6615f7ea5');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.loc[merged_df['compound']< 0, 'status']= 'negative'\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sT7Xt6cpd0qm",
        "outputId": "7df38439-9766-4c59-a591-8b9b95fdc96e"
      },
      "execution_count": 287,
      "outputs": [
        {
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              "                                             reviews  id  neg  neu  pos  \\\n",
              "0    Vancouver to Delhi via London. We were booke...   1  0.1  0.8  0.0   \n",
              "1    Old A320 with narrow pitch. Flight perfectly...   2  0.0  0.7  0.3   \n",
              "2    Another BA Shambles. Started off well with e...   3  0.1  0.7  0.2   \n",
              "3    BA cancelled my flight home to Heathrow on D...   4  0.2  0.8  0.0   \n",
              "4    BA cancelled my flight home, the last flight...   5  0.1  0.8  0.1   \n",
              "\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "merged_df.loc[merged_df['compound']> 0.5, 'status']= 'positive'\n"
      ],
      "metadata": {
        "id": "HHQtiBAfeSJp"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reviews_feedback= merged_df.groupby(by= 'status').count().reviews\n",
        "print(reviews_feedback)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1cCmd6otgpwy",
        "outputId": "57ed10e7-0602-4a5e-c9c3-d3a5ec5de3e6"
      },
      "execution_count": 318,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "status\n",
            "negative    450\n",
            "neutral     103\n",
            "positive    447\n",
            "Name: reviews, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig= px.bar(x= reviews_feedback.index,\n",
        "            y= reviews_feedback.values,\n",
        "            title= \"Reviews by category\",\n",
        "\n",
        ")\n",
        "fig.update_layout(xaxis_title= \"Reviews\",\n",
        "                  yaxis_title= \"Number of Reviews\")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
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      "execution_count": 345,
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