Text_Mining.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOMPDpODjxyM676O485jXrQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ememdeee/Python-Artificial-Intelligence-Stuff/blob/main/Text_Mining.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmRBRzMFVe3G"
      },
      "source": [
        "#CHRISTOPHER\t\t\t\t311810010\n",
        "#MUHAMMAD BASURAH\t\t311810024"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2v2TfVGIwTCf",
        "outputId": "94046cbc-f685-48e2-b8dc-66a34d8e6df6"
      },
      "source": [
        "!pip install nltk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJGFOQLwwr68"
      },
      "source": [
        "import nltk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bimcNPoxubU",
        "outputId": "039e80a1-4019-4701-daa2-507021511079"
      },
      "source": [
        "nltk.download('punkt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhCCVgFTwvb7",
        "outputId": "66ff89c0-221a-4de1-caf5-915e229f7d96"
      },
      "source": [
        "from nltk.tokenize import sent_tokenize\n",
        "text=\"\"\"Hello Mr. Smith, how are you doing today? The weather is great, and city is awesome. The sky is pinkish-blue. You shouldn't eat cardboard\"\"\"\n",
        "tokenized_text=sent_tokenize(text)\n",
        "print(tokenized_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hello Mr. Smith, how are you doing today?', 'The weather is great, and city is awesome.', 'The sky is pinkish-blue.', \"You shouldn't eat cardboard\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFJoLVWux1MH",
        "outputId": "2f35eb1a-4cb8-4f5f-effb-5b994d235952"
      },
      "source": [
        "from nltk.tokenize import word_tokenize\n",
        "tokenized_word=word_tokenize(text)\n",
        "print(tokenized_word)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', ',', 'and', 'city', 'is', 'awesome', '.', 'The', 'sky', 'is', 'pinkish-blue', '.', 'You', 'should', \"n't\", 'eat', 'cardboard']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuMTkZ1Sy4m8",
        "outputId": "900d3dc9-b484-4696-ac48-1dfbc8a21ff4"
      },
      "source": [
        "from nltk.probability import FreqDist\n",
        "fdist = FreqDist(tokenized_word)\n",
        "print(fdist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<FreqDist with 25 samples and 30 outcomes>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCZp_T7QzRSE",
        "outputId": "688924b1-160d-4dc5-d8d8-71c9cd7e79a0"
      },
      "source": [
        "fdist.most_common(5) #menampilkan 5 terbanyak yang common alias kembar ada berapa. begitulah liat hasilnya aja."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('is', 3), (',', 2), ('The', 2), ('.', 2), ('Hello', 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "xqyxQ1YjzaYL",
        "outputId": "04d5ff49-1420-4366-cc8a-6602bd18cda8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fdist.plot(30,cumulative=False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dSjuSsQ0148",
        "outputId": "e8ee7ae5-83f6-4343-a0ed-b8d72c96ea30"
      },
      "source": [
        "nltk.download('stopwords')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKCf7JaV0NAN",
        "outputId": "4630d643-8667-4171-d539-e5186046bc2d"
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "stop_words=set(stopwords.words(\"english\"))\n",
        "#stop_words=set({'?', 'Hello'}) #ini bisa diatur atur\n",
        "print(stop_words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'to', \"needn't\", 'ma', 'he', 'our', 'only', 'has', 'll', 'mightn', 'with', 'am', 'ain', 'up', \"you've\", 'before', 'be', 'into', \"you'll\", \"you're\", 'because', 'itself', 'will', 'm', \"mightn't\", 'off', \"shouldn't\", 'were', 'between', 'doing', 'just', 'himself', 'as', 'hasn', 'can', \"wouldn't\", 'the', 't', \"didn't\", 'me', 'who', 'haven', \"don't\", 'what', 'o', 'him', 'out', 'ourselves', 'from', 'this', 'mustn', 'and', 'on', 'same', 'shan', 'a', 'ours', 'they', \"that'll\", 'so', \"couldn't\", 'why', 'then', 'y', 'weren', 'my', 'down', 'd', 'whom', 'myself', 'in', 'too', 'been', 'didn', 'such', 'that', 'once', 'do', 'these', 'again', 'when', 'very', \"you'd\", 'all', 's', 'if', 'below', 'other', 'after', 'about', 'where', 'doesn', \"it's\", 've', 'it', 'hers', 'now', 'her', 'won', 'them', 'not', 'over', 'themselves', 'for', 'above', 'while', 'yourself', 'herself', 'at', 'some', \"haven't\", \"isn't\", \"hasn't\", 'by', 'further', \"shan't\", 'there', 'both', 'their', \"wasn't\", \"should've\", 'shouldn', 'or', \"aren't\", 'few', 'needn', 'than', 'does', 'through', 'i', \"weren't\", 'isn', 'is', 'don', 'an', 'yourselves', 'couldn', 'hadn', 'being', 'during', 'here', 'your', 'against', \"mustn't\", 'are', 'most', 'his', 'aren', 'did', 'any', 'had', 'should', 'yours', 'which', \"won't\", 'of', 'no', 'having', 'wasn', 'was', 'how', \"hadn't\", 'until', 'have', 'its', \"she's\", 'those', 'you', 're', \"doesn't\", 'theirs', 'she', 'we', 'more', 'wouldn', 'each', 'own', 'nor', 'under', 'but'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olrWcEKQ1jVb",
        "outputId": "5b75f8bf-a3f8-48d9-f66c-646717366d28"
      },
      "source": [
        "text2=\"\"\"Hello Mr. Smith, how are you doing today?\"\"\"\n",
        "tokenized_sent=word_tokenize(text2)\n",
        "print(tokenized_sent)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm3NffYu09-v",
        "outputId": "5d275176-3f09-4cf7-ec6c-392f41a98165"
      },
      "source": [
        "filtered_sent=[]\n",
        "for w in tokenized_sent:\n",
        "    if w not in stop_words:\n",
        "        filtered_sent.append(w)\n",
        "print(\"Tokenized Sentence:\",tokenized_sent)\n",
        "print(\"Filterd Sentence:\",filtered_sent)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized Sentence: ['Hello', 'Mr.', 'Smith', ',', 'how', 'are', 'you', 'doing', 'today', '?']\n",
            "Filterd Sentence: ['Hello', 'Mr.', 'Smith', ',', 'today', '?']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BX9gHfe4HH7",
        "outputId": "877d2f79-6f4b-4496-d355-de716c137e71"
      },
      "source": [
        "# Stemming\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "\n",
        "ps = PorterStemmer()\n",
        "\n",
        "stemmed_words=[]\n",
        "for w in filtered_sent:\n",
        "    stemmed_words.append(ps.stem(w))\n",
        "\n",
        "print(\"Filtered Sentence:\",filtered_sent)\n",
        "print(\"Stemmed Sentence:\",stemmed_words)\n",
        "\n",
        "#ta coba kasi conected beneran bisa\n",
        "#Filtered Sentence: ['Hello', 'Mr.', 'Smith', ',', 'today', 'connected', '?']\n",
        "#Stemmed Sentence: ['hello', 'mr.', 'smith', ',', 'today', 'connect', '?']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtered Sentence: ['Hello', 'Mr.', 'Smith', ',', 'today', '?']\n",
            "Stemmed Sentence: ['hello', 'mr.', 'smith', ',', 'today', '?']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pME73H-xh5oA",
        "outputId": "c5840a2f-3028-43a7-cb38-88f8efa71e01"
      },
      "source": [
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sLVDAsd4LjM",
        "outputId": "3e8a6ac4-0c3e-4749-cb42-d5a9ff0b8bb2"
      },
      "source": [
        "#Lexicon Normalization\n",
        "#performing stemming and Lemmatization\n",
        "#lebih baik menggunakan lemmatization karena melihat word seblumnya\n",
        "\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "lem = WordNetLemmatizer()\n",
        "\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "stem = PorterStemmer()\n",
        "\n",
        "word = \"flying\"\n",
        "print(\"Lemmatized Word:\",lem.lemmatize(word,\"v\"))\n",
        "print(\"Stemmed Word:\",stem.stem(word))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lemmatized Word: fly\n",
            "Stemmed Word: fli\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmtJc82FiRDx",
        "outputId": "a355e445-6347-48e3-ebc4-601b71546c80"
      },
      "source": [
        "sent = \"Albert Einstein was born in Ulm, Germany in 1879.\"\n",
        "tokens=nltk.word_tokenize(sent)\n",
        "print(tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Albert', 'Einstein', 'was', 'born', 'in', 'Ulm', ',', 'Germany', 'in', '1879', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HS1J7IMRiaz4",
        "outputId": "e49ab3dc-5a44-4df9-8050-e756fe831aa8"
      },
      "source": [
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVZpIijmiV14",
        "outputId": "bb105233-d1fc-428f-f688-4e5d21c1af69"
      },
      "source": [
        "nltk.pos_tag(tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Albert', 'NNP'),\n",
              " ('Einstein', 'NNP'),\n",
              " ('was', 'VBD'),\n",
              " ('born', 'VBN'),\n",
              " ('in', 'IN'),\n",
              " ('Ulm', 'NNP'),\n",
              " (',', ','),\n",
              " ('Germany', 'NNP'),\n",
              " ('in', 'IN'),\n",
              " ('1879', 'CD'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR04fEOpp4gY"
      },
      "source": [
        "# Import pandas\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnZ3RiSbp7l_"
      },
      "source": [
        "data=pd.read_csv('train.tsv', sep='\\t')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "VJ83kAgnqaw3",
        "outputId": "430e5cb2-cb60-4ff1-b1a8-a2bae8ecff86"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>PhraseId</th>\n",
              "      <th>SentenceId</th>\n",
              "      <th>Phrase</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A series of escapades demonstrating the adage ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>A series of escapades demonstrating the adage ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>A series</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>series</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PhraseId  ...  Sentiment\n",
              "0         1  ...          1\n",
              "1         2  ...          2\n",
              "2         3  ...          2\n",
              "3         4  ...          2\n",
              "4         5  ...          2\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NO-FwBBAqcRf",
        "outputId": "8c3ef046-103b-48ab-a6f6-80f5dbde884b"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 156060 entries, 0 to 156059\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count   Dtype \n",
            "---  ------      --------------   ----- \n",
            " 0   PhraseId    156060 non-null  int64 \n",
            " 1   SentenceId  156060 non-null  int64 \n",
            " 2   Phrase      156060 non-null  object\n",
            " 3   Sentiment   156060 non-null  int64 \n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyXH_Zdjqeff",
        "outputId": "ccd21964-219e-4950-b661-3951b99ce155"
      },
      "source": [
        "data.Sentiment.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    79582\n",
              "3    32927\n",
              "1    27273\n",
              "4     9206\n",
              "0     7072\n",
              "Name: Sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "IjVxnkC_qhBn",
        "outputId": "50f86624-fa85-4156-a15a-f7868474e105"
      },
      "source": [
        "Sentiment_count=data.groupby('Sentiment').count()\n",
        "plt.bar(Sentiment_count.index.values, Sentiment_count['Phrase'])\n",
        "plt.xlabel('Review Sentiments')\n",
        "plt.ylabel('Number of Review')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3X2KyaOrdGY"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "#tokenizer to remove unwanted elements from out data like symbols and numbers\n",
        "token = RegexpTokenizer(r'[a-zA-Z0-9]+')\n",
        "cv = CountVectorizer(lowercase=True,stop_words='english',ngram_range = (1,1),tokenizer = token.tokenize)\n",
        "text_counts= cv.fit_transform(data['Phrase'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-NqlkJfrj5Z"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    text_counts, data['Sentiment'], test_size=0.3, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKJ9JFUBrpeg",
        "outputId": "dcc71745-a797-4df0-fab1-e9f838ee9832"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "# Model Generation Using Multinomial Naive Bayes\n",
        "clf = MultinomialNB().fit(X_train, y_train)\n",
        "predicted= clf.predict(X_test)\n",
        "print(\"MultinomialNB Accuracy:\",metrics.accuracy_score(y_test, predicted))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MultinomialNB Accuracy: 0.6049169122986885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIs41ezzUZ89"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tf=TfidfVectorizer()\n",
        "text_tf= tf.fit_transform(data['Phrase'])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}