SentimentAnalysis_IMDBReviews.ipynb

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      "cell_type": "markdown",
      "source": [
        "Anita Soroush"
      ],
      "metadata": {
        "id": "EueVySeTthSQ"
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    {
      "cell_type": "markdown",
      "source": [
        "# About Dataset\n",
        "This project is a sentiment analysis of 50k movie reviews on Internet Movie Database (IMDB). This is a dataset for binary sentiment classification and each review has a label of \"positive\" or \"negative\".\n",
        "\n",
        "the dataset is available on: [IMDB dataset of 50k movie reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)\n"
      ],
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        "id": "GNfdMLyohahv"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries"
      ],
      "metadata": {
        "id": "I_MAG2AfcxXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "A3Ac4vjNfiyA"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/gdrive/MyDrive/IMDB_Dataset.csv\")\n",
        "dataset"
      ],
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            "text/plain": [
              "                                                  review sentiment\n",
              "0      One of the other reviewers has mentioned that ...  positive\n",
              "1      A wonderful little production. <br /><br />The...  positive\n",
              "2      I thought this was a wonderful way to spend ti...  positive\n",
              "3      Basically there's a family where a little boy ...  negative\n",
              "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "...                                                  ...       ...\n",
              "49995  I thought this movie did a down right good job...  positive\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
              "49997  I am a Catholic taught in parochial elementary...  negative\n",
              "49998  I'm going to have to disagree with the previou...  negative\n",
              "49999  No one expects the Star Trek movies to be high...  negative\n",
              "\n",
              "[50000 rows x 2 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
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              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>positive</td>\n",
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              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>I am a Catholic taught in parochial elementary...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>I'm going to have to disagree with the previou...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>No one expects the Star Trek movies to be high...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dict(dataset[\"sentiment\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MZJubFuaXbA",
        "outputId": "ff29fd17-ff1a-462c-eaf8-81d9110d0409"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'positive': 25000, 'negative': 25000}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "0KgijYhYfcqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['sentiment'].replace({'positive':1, 'negative':0},inplace=True)"
      ],
      "metadata": {
        "id": "MLGzILscdl9b"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lower Casing"
      ],
      "metadata": {
        "id": "HNbdRAOQLLth"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1: lowercasing:\n",
        "def to_lower(text):\n",
        "    return text.lower()\n",
        "    \n",
        "dataset['review'] = dataset['review'].apply(to_lower)"
      ],
      "metadata": {
        "id": "2qEMy-XE4ZIh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing URLs"
      ],
      "metadata": {
        "id": "scvc_UbALhU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# removing URLs:\n",
        "def remove_url(text):\n",
        "    re_url = re.compile('https?://\\S+|www\\.\\S+')\n",
        "    return re_url.sub('', text)\n",
        "    \n",
        "dataset['review'] = dataset['review'].apply(remove_url)"
      ],
      "metadata": {
        "id": "3Lozfjl16YRI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing Punctuations"
      ],
      "metadata": {
        "id": "BQ6X6H5SLxt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# removing punctuations:\n",
        "import string\n",
        "exclude = string.punctuation\n",
        "def remove_punc(text):\n",
        "    return text.translate(str.maketrans('', '', exclude))\n",
        "    \n",
        "dataset['review'] = dataset['review'].apply(remove_punc)"
      ],
      "metadata": {
        "id": "zIR6yvCw99UC"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing Stopwords"
      ],
      "metadata": {
        "id": "-usNudYZLx4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download(\"popular\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "889nLdn3ZawU",
        "outputId": "b837853f-3c6b-45bf-88d4-89e2d491925a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gazetteers.zip.\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/genesis.zip.\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gutenberg.zip.\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/inaugural.zip.\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/names.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/shakespeare.zip.\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/treebank.zip.\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet2021 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet31 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection popular\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing stopwords:\n",
        "from nltk.corpus import stopwords\n",
        "stopwords_english = stopwords.words('english')\n",
        "def remove_stopwords(text):\n",
        "    new_text = []\n",
        "    for word in text.split():\n",
        "        if word in stopwords_english:\n",
        "            continue\n",
        "        else:\n",
        "            new_text.append(word)\n",
        "    return ' '.join(new_text)\n",
        "\n",
        "dataset['review'] = dataset['review'].apply(remove_stopwords)"
      ],
      "metadata": {
        "id": "9EKpR6vK_wcm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling Imojies"
      ],
      "metadata": {
        "id": "XTocoyplLh_-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install emoji"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCcIxSATaaE9",
        "outputId": "268edba7-a75f-40b5-a2ac-1314b2b3f68c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting emoji\n",
            "  Downloading emoji-2.1.0.tar.gz (216 kB)\n",
            "\u001b[K     |████████████████████████████████| 216 kB 4.0 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-2.1.0-py3-none-any.whl size=212392 sha256=74d85062a60a48ae210d16b4f6e249234d8310fbc0b47bdec2af6ff052390319\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/75/99/51c2a119f4cfd3af7b49cc57e4f737bed7e40b348a85d82804\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-2.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# handling emogies:\n",
        "import emoji\n",
        "def remove_emojies(text):\n",
        "    return emoji.demojize(text)\n",
        "\n",
        "dataset['review'] = dataset['review'].apply(remove_emojies)"
      ],
      "metadata": {
        "id": "lawP6gXMBH8l"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1KHjxpl5hVNi",
        "outputId": "7ffbc52c-dd22-4406-bb09-2fcc32a20dce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  review  sentiment\n",
              "0      one reviewers mentioned watching 1 oz episode ...          1\n",
              "1      wonderful little production br br filming tech...          1\n",
              "2      thought wonderful way spend time hot summer we...          1\n",
              "3      basically theres family little boy jake thinks...          0\n",
              "4      petter matteis love time money visually stunni...          1\n",
              "...                                                  ...        ...\n",
              "49995  thought movie right good job wasnt creative or...          1\n",
              "49996  bad plot bad dialogue bad acting idiotic direc...          0\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stemming \n",
        "stemming is to convert words to their root form (like for ‘playing’, ‘played’ there will be only one token after processing: ‘play’ which is root word)"
      ],
      "metadata": {
        "id": "otR_aHCPUklM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()\n",
        "def perform_stemming(text):\n",
        "    new_text = [ps.stem(word) for word in text.split()]\n",
        "    return ' '.join(new_text)\n",
        "\n",
        "dataset['review'] = dataset['review'].apply(remove_emojies)"
      ],
      "metadata": {
        "id": "iFxD8Q3HUlSw"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train-Test Split"
      ],
      "metadata": {
        "id": "Sf1qkEUU7eWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(dataset['review'], dataset['sentiment'],test_size=0.2,random_state=5)"
      ],
      "metadata": {
        "id": "24DnIg4itVWg"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TF-IDF Vectorizer\n",
        "tf–idf means **Term-Trequency** times **Inverse Document-Frequency**:\n",
        "\n",
        "> **TF(t)** = (Number of times term t appears in a document) / (Total number of terms in the document)\n",
        "\n",
        "> **IDF(t)** = log_e(Total number of documents / Number of documents with term t in it)"
      ],
      "metadata": {
        "id": "Px04mzQWDLEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features = 10000) #max_features = 10000 builds a vocabulary that only considers the top 10000 max_features ordered by term frequency across the corpus.\n",
        "x_train = tfidf_vectorizer.fit_transform(x_train)\n",
        "x_train = x_train.toarray()\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vI3OELHz14xE",
        "outputId": "f2e9ce61-f38a-440f-88ff-02b593b2277a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.03179443, ..., 0.05033325, 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building Models\n",
        "I use 3 different classifiers:\n",
        "- Logistic Regression\n",
        "- Random Forest Classifier\n",
        "- Decision Tree Classifier"
      ],
      "metadata": {
        "id": "YLj0nqsIJGEp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def fit_and_prediction(model, x_train, x_test, y_train, y_test):\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred = model.predict(x_test)\n",
        "    print('Accuracy Score : ',metrics.accuracy_score(y_test, y_pred))\n",
        "    print('F1 Score : ',metrics.f1_score(y_test, y_pred, average='weighted'))\n",
        "    cm = metrics.confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
        "    disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
        "    disp.plot()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ktwIQQryM7m5"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before building the model and testing we should transform x_test using the tf-idf vectorizer that is built based on x_test."
      ],
      "metadata": {
        "id": "7gtVEREFPHKx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = tfidf_vectorizer.transform(x_test)\n",
        "x_test = x_test.toarray()"
      ],
      "metadata": {
        "id": "YE5mGIE26Mle"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "AiGKJJcUOgIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "fit_and_prediction(LogisticRegression(), x_train, x_test, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "-wsUyivjOoeb",
        "outputId": "034a1f37-b28c-4d4b-b42b-23e0d4ca231c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score :  0.8941\n",
            "F1 Score :  0.8940669192979381\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Classifier"
      ],
      "metadata": {
        "id": "w6TI8vi-QbLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "fit_and_prediction(RandomForestClassifier(), x_train, x_test, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "cc4CuiI4QbLL",
        "outputId": "c21a45f3-564b-4b55-8968-0a0d5fc1577c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score :  0.851\n",
            "F1 Score :  0.8510043865712296\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Classifier"
      ],
      "metadata": {
        "id": "5z1gbAdIQbZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "fit_and_prediction(DecisionTreeClassifier(), x_train, x_test, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "d-1SELXDQbZP",
        "outputId": "922d8ddf-a63a-4980-f4dc-f893be1f7fb4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score :  0.7169\n",
            "F1 Score :  0.7169177761193748\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Refrences:\n",
        "\n",
        "- https://www.analyticsvidhya.com/blog/2021/08/text-preprocessing-techniques-for-performing-sentiment-analysis/\n",
        "- https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
        "- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html\n"
      ],
      "metadata": {
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}