SA_Movie_review.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sahilnegi1/Natural-Language-Processing/blob/main/SA_Movie_review.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rtbte4dnVCfR",
        "outputId": "38fa238a-8d4a-4b92-b7fa-a7552357f9bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package vader_lexicon to /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] 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 words to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/words.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('maxent_ne_chunker')\n",
        "nltk.download('words')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "eJqcvQgXWwOF"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "\n",
        "\n",
        "def preprocess_text(text):\n",
        "    # Removing irrelevant information\n",
        "    # (Replace this with your own logic for removing irrelevant information)\n",
        "    processed_text = text\n",
        "\n",
        "    # Removing special characters and punctuation\n",
        "    processed_text = processed_text.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "    # Tokenization\n",
        "    tokens = word_tokenize(processed_text)\n",
        "\n",
        "    # Lemmatization\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
        "\n",
        "    # Stop word removal\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    tokens = [token for token in tokens if token.lower() not in stop_words]\n",
        "\n",
        "    # Joining tokens back into a sentence\n",
        "    processed_text = ' '.join(tokens)\n",
        "\n",
        "    return processed_text\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1U5vpt9dWxcc",
        "outputId": "986b9783-b827-4772-bfc4-304e14a4cf19"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Upload a CSV file and copy file path here: /content/movie_review.csv\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Function to process the uploaded CSV file\n",
        "def process_csv(file_path):\n",
        "    try:\n",
        "        # Read the CSV file\n",
        "        df = pd.read_csv(file_path)\n",
        "        \n",
        "        # Perform any desired operations on the data\n",
        "        # For example, print the first few rows of the DataFrame\n",
        "        print(df.head())\n",
        "        \n",
        "        # You can also perform other operations on the data\n",
        "        \n",
        "    except Exception as e:\n",
        "        print(\"Error: \", str(e))\n",
        "\n",
        "# Ask the user to upload a file\n",
        "file_path = input(\"Upload a CSV file and copy file path here: \")\n",
        "\n",
        "# Store the file path in a variable\n",
        "csv_path = file_path\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4loCrc1WzUd",
        "outputId": "12958561-af1f-4350-efda-ac8c8b8075f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MultinomialNB\n",
            "Accuracy: 0.8419384057971014\n",
            "Precision: 0.8593903638151426\n",
            "Recall: 0.8092592592592592\n",
            "F1 Score: 0.8335717691940868\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Load the data\n",
        "data = pd.read_csv(csv_path,encoding='latin1')\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['review_text'], data['sentiment'], test_size=0.3, random_state=42)\n",
        "\n",
        "# Create a CountVectorizer to convert text into numerical features\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "# Fit and transform the training data\n",
        "X_train_vectorized = vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Create the Multinomial Naive Bayes classifier\n",
        "classifier = MultinomialNB()\n",
        "\n",
        "# Train the classifier\n",
        "classifier.fit(X_train_vectorized, y_train)\n",
        "\n",
        "# Transform the testing data\n",
        "X_test_vectorized = vectorizer.transform(X_test)\n",
        "\n",
        "# Predict the sentiment of the testing data\n",
        "predictions = classifier.predict(X_test_vectorized)\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "precision = precision_score(y_test, predictions)\n",
        "recall = recall_score(y_test, predictions)\n",
        "f1 = f1_score(y_test, predictions)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(\"MultinomialNB\")\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "inC_l8rRYTNW",
        "outputId": "cad069d9-6ba8-4791-ab50-5cf487584dd3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------\n",
            "-----------------------------------\n",
            "-----------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------\n",
            "-----------------------------------\n",
            "-----------------------------------\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-1e39be910933>\u001b[0m in \u001b[0;36m<cell line: 29>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m# Generate a word cloud of most frequent words in positive reviews\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mpositive_reviews\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sentiment'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'review_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mpositive_wordcloud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive_reviews\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive_wordcloud\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'off'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \"\"\"\n\u001b[0;32m--> 639\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_from_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_generated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_text\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \"\"\"\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0mwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_from_frequencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mprocess_text\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mstopwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstopwords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollocations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m             \u001b[0mword_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munigrams_and_bigrams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstopwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_plurals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollocation_threshold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m             \u001b[0;31m# remove stopwords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wordcloud/tokenization.py\u001b[0m in \u001b[0;36munigrams_and_bigrams\u001b[0;34m(words, stopwords, normalize_plurals, collocation_threshold)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mbigram_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcounts_bigrams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0mbigram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbigram_string\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mword1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandard_form\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbigram\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0mword2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandard_form\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbigram\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "import networkx as nx\n",
        "import seaborn as sns\n",
        "\n",
        "# Load the data\n",
        "data = pd.read_csv('/content/movie_review.csv')\n",
        "\n",
        "# Create a histogram of sentiment distribution\n",
        "sns.histplot(data=data, x='sentiment')\n",
        "plt.xlabel('Sentiment')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Sentiment Distribution')\n",
        "plt.show()\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "# Create a pie chart of sentiment distribution\n",
        "sentiment_counts = data['sentiment'].value_counts()\n",
        "plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%')\n",
        "plt.title('Sentiment Distribution')\n",
        "plt.show()\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "# Generate a word cloud of most frequent words in positive reviews\n",
        "positive_reviews = data[data['sentiment'] == 1]['review_text']\n",
        "positive_wordcloud = WordCloud().generate(' '.join(positive_reviews))\n",
        "plt.imshow(positive_wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.title('Word Cloud - Positive Reviews')\n",
        "plt.show()\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "print('-----------------------------------')\n",
        "# Generate a network graph of co-occurring words in reviews\n",
        "graph = nx.Graph()\n",
        "reviews = data['review_text']\n",
        "for review in reviews:\n",
        "    words = review.split()\n",
        "    for i in range(len(words) - 1):\n",
        "        word1 = words[i]\n",
        "        word2 = words[i+1]\n",
        "        if not graph.has_edge(word1, word2):\n",
        "            graph.add_edge(word1, word2, weight=1)\n",
        "        else:\n",
        "            graph[word1][word2]['weight'] += 1\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "pos = nx.spring_layout(graph, k=0.3)\n",
        "nx.draw_networkx(graph, pos, with_labels=True, node_color='lightblue', node_size=500, font_size=8, edge_color='gray', width=0.5)\n",
        "plt.title('Co-occurring Words Network Graph')\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "x2BozoioZwoy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b54cfe39-8ed1-4c61-de2c-30af8c029e6d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the data\n",
        "data = pd.read_csv('/content/movie_review.csv')\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['review_text'], data['sentiment'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a CountVectorizer to convert text into numerical features\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "# Fit and transform the training data\n",
        "X_train_vectorized = vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Create the Multinomial Naive Bayes classifier\n",
        "classifier = MultinomialNB()\n",
        "\n",
        "# Train the classifier\n",
        "classifier.fit(X_train_vectorized, y_train)\n",
        "\n",
        "# Transform the testing data\n",
        "X_test_vectorized = vectorizer.transform(X_test)\n",
        "\n",
        "# Predict the sentiment of the testing data\n",
        "predictions = classifier.predict(X_test_vectorized)\n",
        "\n",
        "# Convert predictions and actual values to data frames\n",
        "df = pd.DataFrame({'Predicted': predictions, 'Actual': y_test})\n",
        "\n",
        "# Plot the actual values\n",
        "plt.scatter(range(len(df)), df['Actual'], marker='*', color='blue', label='Actual')\n",
        "\n",
        "# Plot the predicted values\n",
        "plt.scatter(range(len(df)), df['Predicted'], marker='*', color='red', label='Predicted')\n",
        "\n",
        "# Set x-axis limits and labels\n",
        "plt.xlim(0, len(df))\n",
        "plt.xlabel('Index')\n",
        "\n",
        "# Set y-axis limits and labels\n",
        "plt.ylim(-0.5, 1.5)\n",
        "plt.ylabel('Value')\n",
        "\n",
        "# Set the title and legend\n",
        "plt.title('Actual and Predicted Values')\n",
        "plt.legend()\n",
        "\n",
        "# Display the graph\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LEkgv2Xbsdst"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNeTwc0ANB8UvbzDBRXlCVU",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}