Sentiment_AnalysisDA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1ZUGbRUQXkK"
      },
      "source": [
        "# **End-to-end Sentiment Analysis Example in Python**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VjD4GosQgrl"
      },
      "source": [
        "### **Step 1 - Import libraries and load dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6EO0hCWSOFsN",
        "outputId": "6a01b37c-29c6-4839-c030-f0e0092beb72"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
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            "text/plain": [
              "                                              reviewText  Positive\n",
              "0      This is a one of the best apps acording to a b...         1\n",
              "1      This is a pretty good version of the game for ...         1\n",
              "2      this is a really cool game. there are a bunch ...         1\n",
              "3      This is a silly game and can be frustrating, b...         1\n",
              "4      This is a terrific game on any pad. Hrs of fun...         1\n",
              "...                                                  ...       ...\n",
              "19995  this app is fricken stupid.it froze on the kin...         0\n",
              "19996  Please add me!!!!! I need neighbors! Ginger101...         1\n",
              "19997  love it!  this game. is awesome. wish it had m...         1\n",
              "19998  I love love love this app on my side of fashio...         1\n",
              "19999  This game is a rip off. Here is a list of thin...         0\n",
              "\n",
              "[20000 rows x 2 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "\n",
        "import nltk\n",
        "\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "\n",
        "# download nltk corpus (first time only)\n",
        "import nltk\n",
        "\n",
        "nltk.download('all')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Load the amazon review dataset\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/pycaret/pycaret/master/datasets/amazon.csv')\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3F88sAQpQrdh"
      },
      "source": [
        "### **Step 2 - Preprocess text**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6edPpTyJQVBA",
        "outputId": "866e8678-2c53-44a3-cb84-496465a4b9fa"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>19995</th>\n",
              "      <td>app fricken stupid.it froze kindle wont allow ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19996</th>\n",
              "      <td>please add ! ! ! ! ! need neighbor ! ginger101...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19997</th>\n",
              "      <td>love ! game . awesome . wish free stuff house ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19998</th>\n",
              "      <td>love love love app side fashion story fight wo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19999</th>\n",
              "      <td>game rip . list thing make better &amp; bull ; fir...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20000 rows × 2 columns</p>\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "text/plain": [
              "                                              reviewText  Positive\n",
              "0      one best apps acording bunch people agree bomb...         1\n",
              "1      pretty good version game free . lot different ...         1\n",
              "2      really cool game . bunch level find golden egg...         1\n",
              "3      silly game frustrating , lot fun definitely re...         1\n",
              "4      terrific game pad . hr fun . grandkids love . ...         1\n",
              "...                                                  ...       ...\n",
              "19995  app fricken stupid.it froze kindle wont allow ...         0\n",
              "19996  please add ! ! ! ! ! need neighbor ! ginger101...         1\n",
              "19997  love ! game . awesome . wish free stuff house ...         1\n",
              "19998  love love love app side fashion story fight wo...         1\n",
              "19999  game rip . list thing make better & bull ; fir...         0\n",
              "\n",
              "[20000 rows x 2 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# create preprocess_text function\n",
        "def preprocess_text(text):\n",
        "\n",
        "    # Tokenize the text\n",
        "\n",
        "    tokens = word_tokenize(text.lower())\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Remove stop words\n",
        "\n",
        "    filtered_tokens = [token for token in tokens if token not in stopwords.words('english')]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Lemmatize the tokens\n",
        "\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Join the tokens back into a string\n",
        "\n",
        "    processed_text = ' '.join(lemmatized_tokens)\n",
        "\n",
        "    return processed_text\n",
        "\n",
        "# apply the function df\n",
        "\n",
        "df['reviewText'] = df['reviewText'].apply(preprocess_text)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atDcnacLQzD1"
      },
      "source": [
        "### **Step 3 - NLTK Sentiment Analyzer**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5E6L0rr-Qw2b",
        "outputId": "eec9ba76-641d-4971-f892-35a9cd625339"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20000,\n  \"fields\": [\n    {\n      \"column\": \"reviewText\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19997,\n        \"samples\": [\n          \"found looking . might alright someone like shop town .\",\n          \"use mine time kindle fire . simple easy use . complaint .\",\n          \"dear creaters , think age science.with new element atom.stuff like plutonuim gold uranuim eft . game rock !\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Positive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-2f99e7e8-f60f-438f-8b63-1a329feb4495\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              reviewText  Positive  sentiment\n",
              "0      one best apps acording bunch people agree bomb...         1          1\n",
              "1      pretty good version game free . lot different ...         1          1\n",
              "2      really cool game . bunch level find golden egg...         1          1\n",
              "3      silly game frustrating , lot fun definitely re...         1          1\n",
              "4      terrific game pad . hr fun . grandkids love . ...         1          1\n",
              "...                                                  ...       ...        ...\n",
              "19995  app fricken stupid.it froze kindle wont allow ...         0          0\n",
              "19996  please add ! ! ! ! ! need neighbor ! ginger101...         1          1\n",
              "19997  love ! game . awesome . wish free stuff house ...         1          1\n",
              "19998  love love love app side fashion story fight wo...         1          1\n",
              "19999  game rip . list thing make better & bull ; fir...         0          1\n",
              "\n",
              "[20000 rows x 3 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# initialize NLTK sentiment analyzer\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "\n",
        "\n",
        "# create get_sentiment function\n",
        "\n",
        "def get_sentiment(text):\n",
        "\n",
        "    scores = analyzer.polarity_scores(text)\n",
        "\n",
        "    sentiment = 1 if scores['pos'] > 0 else 0\n",
        "\n",
        "    return sentiment\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# apply get_sentiment function\n",
        "\n",
        "df['sentiment'] = df['reviewText'].apply(get_sentiment)\n",
        "\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EiKCd0HaWVSH",
        "outputId": "62318a75-3cc9-41f6-9e73-3c83797a0f82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 1131  3636]\n",
            " [  576 14657]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Assuming 'actual' and 'predicted' columns are in your dataframe\n",
        "y_true = df['Positive']\n",
        "y_pred = df['sentiment']\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(cm)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJrg5ivUXehu"
      },
      "outputs": [],
      "source": [
        "# This confusion matrix corresponds to a binary classification problem. Here's what it indicates:\n",
        "\n",
        "# The model has correctly identified 1131 true negative and 14657 true positive instances.\n",
        "# The model has made 3636 false positive predictions (incorrectly labeling negative instances as positive).\n",
        "# The model has made 576 false negative predictions (incorrectly labeling positive instances as negative)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Qswz-l72ZDil",
        "outputId": "9976e232-f56a-466d-f645-653bc29ee257"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
        "\n",
        "# Assuming 'df' has the columns 'Positive' (actual sentiment) and 'sentiment' (predicted sentiment)\n",
        "\n",
        "# Map 'Positive' to 1 and 'Negative' to 0\n",
        "y_true = df['Positive']\n",
        "y_pred = df['sentiment']\n",
        "\n",
        "# 1. Confusion Matrix (Heatmap)\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Plot confusion matrix as heatmap\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n",
        "\n",
        "# 2. Classification Report (Precision, Recall, F1-Score)\n",
        "report = classification_report(y_true, y_pred, output_dict=True)\n",
        "report_df = pd.DataFrame(report).transpose()\n",
        "\n",
        "# Plot classification report metrics\n",
        "report_df[['precision', 'recall', 'f1-score']].plot(kind='bar', figsize=(10, 6))\n",
        "plt.title('Classification Report Metrics')\n",
        "plt.xlabel('Classes')\n",
        "plt.ylabel('Scores')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()\n",
        "\n",
        "# 3. ROC Curve (Receiver Operating Characteristic Curve)\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
        "plt.title('Receiver Operating Characteristic Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "# 4. Accuracy Plot\n",
        "accuracy = (y_true == y_pred).mean()\n",
        "\n",
        "# Plot accuracy as a bar chart\n",
        "plt.figure(figsize=(5, 4))\n",
        "plt.bar(['Accuracy'], [accuracy], color='green')\n",
        "plt.ylim(0, 1)\n",
        "plt.title(f'Accuracy: {accuracy:.2f}')\n",
        "plt.ylabel('Score')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NU1VJz1cBY6S"
      },
      "source": [
        "# **`IBM SME NLP`**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GN0zjI2qBvBV"
      },
      "source": [
        "# **Tokenization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oanN6wQMBhWO",
        "outputId": "28d0f74e-61ef-43f2-ca0b-d754798e34da"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['i', 'am', 'oshin', 'and', 'i', 'like', 'to', 'study', 'data', 'analytics']\n"
          ]
        }
      ],
      "source": [
        "from nltk.tokenize import word_tokenize\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('punkt_tab')\n",
        "\n",
        "sentence='i am oshin and i like to study data analytics'\n",
        "\n",
        "tokens=word_tokenize(sentence)\n",
        "print(tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXStjYUcC1QP"
      },
      "source": [
        "# **Stopwords**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za6e9Q7jCpi6",
        "outputId": "887da3e2-971e-4e78-f1c7-f047799d9cb5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'yourself', \"isn't\", 'will', 'she', 'mustn', 'on', 'about', 'haven', 'all', \"weren't\", \"needn't\", 'off', 'here', 'as', 'same', 'myself', 'that', 'than', 'these', 'her', \"haven't\", 's', 'ours', 'while', 'through', \"wouldn't\", 'he', 'which', 'himself', 'have', 'or', 'the', 'up', 'only', 'll', 'weren', 'with', 'do', \"she's\", 'because', 'theirs', 'such', 'can', \"don't\", 'over', 'yourselves', 'doing', 'me', 'shan', 'being', \"you're\", \"you'd\", 'its', 'nor', 'were', 'under', 'needn', 'you', 'no', \"you'll\", 'those', 'doesn', \"couldn't\", \"that'll\", 'herself', 'an', \"wasn't\", 'further', 'wouldn', \"didn't\", 'to', 'some', 'they', 'other', 'don', 'there', 'where', 'this', 'between', 'most', 'hers', 'when', 're', 'now', 'it', 'our', \"it's\", 'them', 've', 'm', 'him', 'y', 'yours', \"mustn't\", 'itself', 'been', 'ourselves', 'aren', 'o', 'are', 'before', 'more', \"should've\", \"mightn't\", 'very', 'own', 'just', \"shouldn't\", 'my', 'mightn', 'hasn', 'be', 'by', \"won't\", 'a', 'above', 'themselves', 'below', 'am', 'after', 'who', 'did', \"doesn't\", 'few', 'out', 'is', 'we', 'does', 'had', 'if', 'too', 'wasn', 'for', 't', 'i', 'how', 'both', 'not', 'during', 'again', 'then', 'hadn', 'should', 'in', 'any', 'his', 'ain', 'but', 'their', 'into', 'each', 'whom', 'has', \"you've\", 'your', \"aren't\", \"shan't\", 'what', 'from', 'once', 'down', 'so', 'against', 'couldn', 'was', 'ma', 'won', 'why', 'didn', \"hadn't\", 'd', 'isn', 'having', 'of', \"hasn't\", 'until', 'and', 'at', 'shouldn'}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "stopwords=set(stopwords.words('english'))\n",
        "print(stopwords)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rsK8GaRDwew",
        "outputId": "f08118e5-e7c3-4bf8-af87-e78f58f38a9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['oshin', 'like', 'study', 'data', 'analytics']\n",
            "['oshin', 'like', 'study', 'data', 'analytics']\n",
            "['oshin', 'like', 'study', 'data', 'analytics']\n",
            "['oshin', 'like', 'study', 'data', 'analytics']\n",
            "['oshin', 'like', 'study', 'data', 'analytics']\n"
          ]
        }
      ],
      "source": [
        "reviews= ['I love dancing','Bye its my time to go','hey wassup','goodluck', 'have a better day tomorrow']\n",
        "for review in reviews:\n",
        "  tokenns=word_tokenize(review)\n",
        "\n",
        "  #Remove stop words\n",
        "  filtered_tokens=[token for token in tokens if token.lower() not in stopwords]\n",
        "  print(filtered_tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrO9egKUFKj2"
      },
      "source": [
        "# **Lemmatization & Stemming**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "saVAvNs9GDfd"
      },
      "source": [
        "# **Bag of words**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1-JFjCpFPAh",
        "outputId": "6e271590-761c-47a2-82ee-63b5cdf06db2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "'I am in great spirit': negative\n",
            "'I am in great spirit': positive\n",
            "'I am in great spirit': neutral\n"
          ]
        }
      ],
      "source": [
        "from textblob import TextBlob\n",
        "import nltk\n",
        "\n",
        "import pandas as pd\n",
        "from textblob import TextBlob\n",
        "\n",
        "tokens=word_tokenize('review')\n",
        "#rule based sentiment analysis\n",
        "#define + and - words\n",
        "positive_words=['great','love','beautiful']\n",
        "negative_words=['bad','hate','ugly']\n",
        "def analyze_sentiment_rule_based(review):\n",
        "  tokens= word_tokenize(review.lower())\n",
        "  positive_count= sum(1 for token in tokens if token in positive_words)\n",
        "  negative_count=sum(1 for token in tokens if token in negative_words)\n",
        "  if positive_count>negative_count:\n",
        "    return 'positive'\n",
        "  elif positive_count<negative_count:\n",
        "    return 'negative'\n",
        "  else:\n",
        "    return 'neutral'\n",
        "\n",
        "reviews=['i hate this app','its very great','its an app']\n",
        "for review in reviews:\n",
        "  print(f\"'I am in great spirit': {analyze_sentiment_rule_based(review)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBrYIMeIJeDL",
        "outputId": "a6d07eb3-1c83-4de0-dc93-7b66a9e8d645"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original Review: I hate this app\n",
            "Tokens: ['I', 'hate', 'this', 'app']\n",
            "Stemmed Tokens: ['i', 'hate', 'thi', 'app']\n",
            "Lemmatized Tokens: ['I', 'hate', 'this', 'app']\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Download required resources\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "# Example reviews\n",
        "reviews = ['I hate this app']\n",
        "\n",
        "# Initialize the stemmer and lemmatizer\n",
        "stemmer = PorterStemmer()\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "for review in reviews:\n",
        "    # Tokenize the review\n",
        "    tokens = word_tokenize(review)\n",
        "\n",
        "    # Stem the tokens\n",
        "    stemmed_tokens = [stemmer.stem(token) for token in tokens]\n",
        "\n",
        "    # Lemmatize the tokens\n",
        "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
        "\n",
        "    # Print the results\n",
        "    print(f\"Original Review: {review}\")\n",
        "    print(f\"Tokens: {tokens}\")\n",
        "    print(f\"Stemmed Tokens: {stemmed_tokens}\")\n",
        "    print(f\"Lemmatized Tokens: {lemmatized_tokens}\")\n",
        "    print()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HRt3d-8CLz3f"
      },
      "outputs": [],
      "source": [
        "from textblob import TextBlob\n",
        "\n",
        "def sentiment(reviews):\n",
        "  for review in reviews:\n",
        "    score=TextBlob(review).sentiment.polarity\n",
        "    print(score)\n",
        "    if score>0:\n",
        "      print(f\"'{review}':positive\")\n",
        "    elif score<0:\n",
        "      print(f\"'{review}':negative\")\n",
        "    else:\n",
        "      print(f\"'{review}':neutral\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXcccb5Tqw4l"
      },
      "source": [
        "# **`Neural Network Based`**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPE9S01MNtCw",
        "outputId": "9c907cd1-baa7-4e72-8c7b-e19ac187bc42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sentence: very bad class\n",
            "Sentiment: -1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "2/2 - 7s - 3s/step - accuracy: 0.5000 - loss: 0.6909 - val_accuracy: 0.0000e+00 - val_loss: 0.6961\n",
            "Epoch 2/5\n",
            "2/2 - 1s - 670ms/step - accuracy: 0.7500 - loss: 0.6854 - val_accuracy: 0.0000e+00 - val_loss: 0.6989\n",
            "Epoch 3/5\n",
            "2/2 - 0s - 68ms/step - accuracy: 0.7500 - loss: 0.6792 - val_accuracy: 0.0000e+00 - val_loss: 0.7020\n",
            "Epoch 4/5\n",
            "2/2 - 0s - 63ms/step - accuracy: 0.7500 - loss: 0.6718 - val_accuracy: 0.0000e+00 - val_loss: 0.7050\n",
            "Epoch 5/5\n",
            "2/2 - 0s - 45ms/step - accuracy: 0.7500 - loss: 0.6633 - val_accuracy: 0.0000e+00 - val_loss: 0.7080\n",
            "Accuracy: 0.00%\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344ms/step\n",
            "Predicted sentiment for 'very bad class': Positive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from nltk.tokenize import word_tokenize\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Step 1: Tokenize the sentence\n",
        "positive = ['good', 'amazing']\n",
        "negative = ['bad', 'poor']\n",
        "\n",
        "sen = 'very bad class'\n",
        "\n",
        "# Tokenize the sentence\n",
        "tokens = word_tokenize(sen.lower())  # Tokenize and convert to lowercase\n",
        "\n",
        "# Step 2: Check for the presence of positive or negative words\n",
        "sentiment = 0  # Start with neutral sentiment\n",
        "for word in tokens:\n",
        "    if word in positive:\n",
        "        sentiment = 1  # Positive sentiment\n",
        "    elif word in negative:\n",
        "        sentiment = -1  # Negative sentiment\n",
        "\n",
        "print(f\"Sentence: {sen}\")\n",
        "print(f\"Sentiment: {sentiment}\")  # 1 for Positive, -1 for Negative, 0 for Neutral\n",
        "\n",
        "\n",
        "# Now, let's build a simple neural network based on the toy example\n",
        "\n",
        "# Example dataset (you would normally use a larger dataset for training)\n",
        "texts = ['good movie', 'bad experience', 'amazing product', 'poor quality', 'good quality']\n",
        "labels = [1, -1, 1, -1, 1]  # 1 for Positive, -1 for Negative\n",
        "\n",
        "# Step 3: Tokenize and preprocess the texts\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=5000, lower=True)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "X = tokenizer.texts_to_sequences(texts)\n",
        "X = pad_sequences(X, padding='post')\n",
        "\n",
        "# Step 4: Encode the labels (Positive/Negative)\n",
        "encoder = LabelEncoder()\n",
        "y = encoder.fit_transform(labels)\n",
        "\n",
        "# Step 5: Split the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Step 6: Build the neural network model\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=5000, output_dim=128, input_length=X.shape[1]))\n",
        "model.add(SpatialDropout1D(0.2))\n",
        "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))  # Binary classification (Positive/Negative)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Step 7: Train the model\n",
        "model.fit(X_train, y_train, epochs=5, batch_size=2, validation_data=(X_test, y_test), verbose=2)\n",
        "\n",
        "# Step 8: Evaluate the model\n",
        "score, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(f\"Accuracy: {accuracy*100:.2f}%\")\n",
        "\n",
        "# Step 9: Predict sentiment for the test sentence\n",
        "test_sentence = 'very bad class'\n",
        "test_sequence = tokenizer.texts_to_sequences([test_sentence])\n",
        "test_sequence = pad_sequences(test_sequence, padding='post', maxlen=X.shape[1])\n",
        "\n",
        "pred = model.predict(test_sequence)\n",
        "print(f\"Predicted sentiment for '{test_sentence}': {'Positive' if pred[0] > 0.5 else 'Negative'}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "j7P7JBTLa5Gd",
        "outputId": "e249708e-4e6c-44e1-a195-74e0205a2957"
      },
      "outputs": [
        {
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
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              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ spatial_dropout1d_2                  │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)                   │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
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              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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              "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)                   │                             │                 │\n",
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              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 21ms/step - accuracy: 0.7069 - loss: 0.6919\n",
            "Epoch 2/5\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.8306 - loss: 0.6873\n",
            "Epoch 3/5\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8514 - loss: 0.6751\n",
            "Epoch 4/5\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.8306 - loss: 0.6691\n",
            "Epoch 5/5\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7056 - loss: 0.6727\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329ms/step\n",
            "Predicted sentiment for 'very bad class': Negative\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Embedding, SpatialDropout1D\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "\n",
        "# Sample text data and labels (use more data for better performance)\n",
        "texts = [\n",
        "    \"This is an amazing product\", \"I love this phone\", \"The product is bad\", \"Worst purchase ever\",\n",
        "    \"great experience\", \"horrible service\", \"I like this\", \"I dislike this\", \"amazing quality\", \"bad quality\"\n",
        "]\n",
        "labels = [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]  # 1 for Positive, 0 for Negative sentiment\n",
        "\n",
        "# Initialize the tokenizer\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=5000, lower=True)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "# Convert texts to sequences\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "# Pad sequences to ensure they all have the same length\n",
        "X = pad_sequences(sequences, padding='post')\n",
        "\n",
        "# Labels (binary sentiment classification)\n",
        "y = np.array(labels)  # Ensure labels are in numpy array format\n",
        "\n",
        "# Build the neural network model\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=5000, output_dim=128))  # Embedding layer to convert words to vectors\n",
        "model.add(SpatialDropout1D(0.2))  # Dropout to avoid overfitting\n",
        "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))  # LSTM layer for sequential data\n",
        "model.add(Dense(1, activation='sigmoid'))  # Output layer with sigmoid activation for binary classification\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Print model summary\n",
        "model.summary()\n",
        "\n",
        "# Train the model (with more epochs and batch size suitable for the dataset)\n",
        "model.fit(X, y, epochs=5, batch_size=2)\n",
        "\n",
        "# Test sentence for prediction\n",
        "test_sentence = [\"very bad class\"]\n",
        "test_seq = tokenizer.texts_to_sequences(test_sentence)\n",
        "test_seq = pad_sequences(test_seq, padding='post', maxlen=X.shape[1])  # Ensure the shape is correct\n",
        "\n",
        "# Make the prediction\n",
        "prediction = model.predict(test_seq)\n",
        "\n",
        "# Apply threshold to classify the sentiment (0.5 as the threshold)\n",
        "if prediction >= 0.5:\n",
        "    print(f\"Predicted sentiment for '{test_sentence[0]}': Positive\")\n",
        "else:\n",
        "    print(f\"Predicted sentiment for '{test_sentence[0]}': Negative\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttVc9cxnb3aX",
        "outputId": "4eb3eb0c-3732-47d7-c17b-d018cc7eda86"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m235s\u001b[0m 46ms/step - accuracy: 0.8489 - loss: 0.3557\n",
            "Epoch 2/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 46ms/step - accuracy: 0.9189 - loss: 0.2014\n",
            "Epoch 3/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 46ms/step - accuracy: 0.9355 - loss: 0.1659\n",
            "Epoch 4/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m270s\u001b[0m 48ms/step - accuracy: 0.9513 - loss: 0.1292\n",
            "Epoch 5/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m309s\u001b[0m 57ms/step - accuracy: 0.9635 - loss: 0.0954\n",
            "Epoch 6/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 44ms/step - accuracy: 0.9734 - loss: 0.0722\n",
            "Epoch 7/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 45ms/step - accuracy: 0.9787 - loss: 0.0556\n",
            "Epoch 8/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 44ms/step - accuracy: 0.9840 - loss: 0.0439\n",
            "Epoch 9/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m267s\u001b[0m 45ms/step - accuracy: 0.9888 - loss: 0.0341\n",
            "Epoch 10/10\n",
            "\u001b[1m5000/5000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 44ms/step - accuracy: 0.9905 - loss: 0.0270\n",
            "Enter a sentence to predict its sentiment (type 'exit' to quit):\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Embedding, SpatialDropout1D, Bidirectional\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# Assuming df is already loaded with 'reviewText' and 'Positive' columns\n",
        "# For example, df = pd.read_csv('your_dataset.csv')  # Uncomment if loading from CSV file\n",
        "\n",
        "# Assuming df contains 'reviewText' (text data) and 'Positive' (sentiment labels)\n",
        "# df = pd.DataFrame({\n",
        "#     'reviewText': ['I love this product', 'This is terrible', 'Amazing quality', 'Very bad experience', 'Highly recommend'],\n",
        "#     'Positive': [1, 0, 1, 0, 1],  # Actual sentiment: 1 = Positive, 0 = Negative\n",
        "# })\n",
        "\n",
        "# Preprocessing function to clean the text data\n",
        "def preprocess_text(text):\n",
        "    text = text.lower()  # Convert to lowercase\n",
        "    text = re.sub(r'\\d+', '', text)  # Remove numbers\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)  # Remove punctuation\n",
        "    return text\n",
        "\n",
        "# Apply preprocessing to the 'reviewText' column of the dataframe\n",
        "df['reviewText'] = df['reviewText'].apply(preprocess_text)\n",
        "\n",
        "# Split into features (X) and labels (y)\n",
        "texts = df['reviewText'].values\n",
        "labels = df['Positive'].values  # Use 'Positive' column as labels\n",
        "\n",
        "# Initialize the tokenizer\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=5000, lower=True)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "# Convert texts to sequences\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "# Pad sequences to ensure they all have the same length\n",
        "X = pad_sequences(sequences, padding='post')\n",
        "\n",
        "# Labels (binary sentiment classification)\n",
        "y = np.array(labels)  # Ensure labels are in numpy array format\n",
        "\n",
        "# Build the neural network model (Bidirectional LSTM)\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=5000, output_dim=128))  # Embedding layer\n",
        "model.add(SpatialDropout1D(0.2))  # Dropout to avoid overfitting\n",
        "model.add(Bidirectional(LSTM(100, dropout=0.2, recurrent_dropout=0.2)))  # Bidirectional LSTM for better context capture\n",
        "model.add(Dense(1, activation='sigmoid'))  # Output layer with sigmoid activation for binary classification\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Train the model (with more epochs)\n",
        "model.fit(X, y, epochs=10, batch_size=4)\n",
        "\n",
        "# Prediction function\n",
        "def predict_sentiment(sentence):\n",
        "    # Preprocess the input sentence\n",
        "    sentence = preprocess_text(sentence)\n",
        "\n",
        "    # Convert the input sentence to sequence\n",
        "    seq = tokenizer.texts_to_sequences([sentence])\n",
        "\n",
        "    # Pad the sequence to the same length as the training data\n",
        "    padded_seq = pad_sequences(seq, padding='post', maxlen=X.shape[1])\n",
        "\n",
        "    # Predict sentiment\n",
        "    prediction = model.predict(padded_seq)\n",
        "\n",
        "    # Output the result\n",
        "    if prediction >= 0.5:\n",
        "        return \"Positive\"\n",
        "    else:\n",
        "        return \"Negative\"\n",
        "\n",
        "# Interactive input\n",
        "print(\"Enter a sentence to predict its sentiment (type 'exit' to quit):\")\n",
        "\n",
        "while True:\n",
        "    user_input = input(\"> \")\n",
        "\n",
        "    if user_input.lower() == \"exit\":\n",
        "        print(\"Goodbye!\")\n",
        "        break\n",
        "\n",
        "    sentiment = predict_sentiment(user_input)\n",
        "    print(f\"Predicted sentiment for '{user_input}': {sentiment}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jh_Da5z8Nv_a"
      },
      "source": [
        "# **Neural Network-based sentiment analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eicgpL3N2q_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, SpatialDropout1D\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.datasets import imdb\n",
        "\n",
        "# Load the IMDB dataset\n",
        "max_features = 10000  # Number of words to consider as features\n",
        "max_len = 100  # Maximum length of each input sequence\n",
        "\n",
        "# Loading the IMDB dataset from Keras\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
        "\n",
        "# Pad sequences to ensure all input sequences are of the same length\n",
        "x_train = pad_sequences(x_train, maxlen=max_len)\n",
        "x_test = pad_sequences(x_test, maxlen=max_len)\n",
        "\n",
        "# Build the neural network model\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=max_features, output_dim=128, input_length=max_len))\n",
        "model.add(SpatialDropout1D(0.2))\n",
        "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(x_train, y_train, epochs=5, batch_size=64, validation_data=(x_test, y_test), verbose=2)\n",
        "\n",
        "# Evaluate the model\n",
        "score, accuracy = model.evaluate(x_test, y_test, verbose=2)\n",
        "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "# Predict sentiment for a sample review\n",
        "sample_review = \"I loved the movie, it was amazing!\"\n",
        "# Tokenize the sample review (converting words to indices)\n",
        "word_index = imdb.get_word_index()\n",
        "sample_review_tokens = [word_index.get(word, 0) for word in sample_review.lower().split()]\n",
        "# Pad the review to the same length as the training data\n",
        "sample_review_padded = pad_sequences([sample_review_tokens], maxlen=max_len)\n",
        "\n",
        "# Predict the sentiment of the sample review\n",
        "prediction = model.predict(sample_review_padded)\n",
        "sentiment = \"Positive\" if prediction >= 0.5 else \"Negative\"\n",
        "print(f\"Sentiment: {sentiment}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **`Using BERT, Transformers`**"
      ],
      "metadata": {
        "id": "QrAgv7ElOW-a"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rvTGm7UQiiP",
        "outputId": "879f7ec6-775f-4dae-d7b1-a9f7ff4b656b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.9.1)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.11.6)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.67.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.27.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.12.14)\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7GWKAizQiQ0"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcXaU6ekOVWs",
        "outputId": "d5f55f84-be9f-49d8-c6be-cc090c327e46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Package punkt_tab is already up-to-date!\n",
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: I love the new phone.\n",
            "Sentiment: 5 stars, Score: 0.8152029514312744\n",
            "--------------------------------------------------\n",
            "Sentence: The battery life is amazing!\n",
            "Sentiment: 5 stars, Score: 0.8153871893882751\n",
            "--------------------------------------------------\n",
            "Sentence: However, the camera quality could be better.\n",
            "Sentiment: 3 stars, Score: 0.621445894241333\n",
            "--------------------------------------------------\n",
            "Sentence: Oshin.\n",
            "Sentiment: 3 stars, Score: 0.32453295588493347\n",
            "--------------------------------------------------\n",
            "Sentence: Hey look at me.\n",
            "Sentiment: 5 stars, Score: 0.4124619662761688\n",
            "--------------------------------------------------\n",
            "Sentence: Do you remember?\n",
            "Sentiment: 5 stars, Score: 0.26115894317626953\n",
            "--------------------------------------------------\n",
            "Sentence: Please buy me a chocolate.\n",
            "Sentiment: 1 star, Score: 0.4337516129016876\n",
            "--------------------------------------------------\n",
            "Sentence: I hate you idiot.\n",
            "Sentiment: 1 star, Score: 0.7333212494850159\n",
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, pipeline\n",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('punkt_tab')\n",
        "\n",
        "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertForSequenceClassification.from_pretrained(model_name)\n",
        "\n",
        "sentiment_pipeline = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
        "\n",
        "def preprocess_text(text):\n",
        "    sentences = sent_tokenize(text)\n",
        "    return sentences\n",
        "\n",
        "def analyze_sentiment(text):\n",
        "    sentences = preprocess_text(text)\n",
        "    results = []\n",
        "    for sentence in sentences:\n",
        "        sentiment = sentiment_pipeline(sentence)\n",
        "        results.append((sentence, sentiment[0]['label'], sentiment[0]['score']))\n",
        "    return results\n",
        "\n",
        "text = \"I love the new phone. The battery life is amazing! However, the camera quality could be better. Oshin. Hey look at me. Do you remember? Please buy me a chocolate. I hate you idiot.\"\n",
        "sentiment_results = analyze_sentiment(text)\n",
        "\n",
        "for sentence, label, score in sentiment_results:\n",
        "    print(f\"Sentence: {sentence}\")\n",
        "    print(f\"Sentiment: {label}, Score: {score}\")\n",
        "    print(\"-\" * 50)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. torch (PyTorch):\n",
        "# Purpose: PyTorch is an open-source machine learning library used for building and training neural networks. It supports dynamic computation graphs, making it easier to experiment with and modify models.\n",
        "# Usage in the code: It's used to run the BERT model for sentiment analysis, as BERT is a neural network-based model implemented in PyTorch.\n",
        "\n",
        "# 2. transformers (Hugging Face):\n",
        "# Purpose: The transformers library by Hugging Face provides state-of-the-art machine learning models like BERT, GPT, T5, etc., pre-trained on large datasets for tasks like text classification, translation, and question answering.\n",
        "# Usage in the code: The library is used to load the pre-trained BERT model (nlptown/bert-base-multilingual-uncased-sentiment), which performs sentiment analysis on text.\n",
        "\n",
        "# 3. nltk (Natural Language Toolkit):\n",
        "# Purpose: NLTK is a Python library used for working with human language data (text). It provides tools for text processing, such as tokenization, part-of-speech tagging, and parsing.\n",
        "# Usage in the code: NLTK’s sent_tokenize() function is used to split the input text into individual sentences for processing by the sentiment analysis model.\n",
        "\n",
        "# 4. BERT Model for Sentiment Analysis (nlptown/bert-base-multilingual-uncased-sentiment):\n",
        "# Purpose: BERT (Bidirectional Encoder Representations from Transformers) is a powerful transformer model for understanding language context. The specific model (nlptown/bert-base-multilingual-uncased-sentiment) is pre-trained to classify the sentiment of text in multiple languages.\n",
        "# Usage in the code: The model is used to classify the sentiment (positive, negative, etc.) of each sentence, returning both the label and the confidence score.\n",
        "\n",
        "# In short, torch runs the neural network, transformers provides the BERT model, and nltk helps with breaking text into sentences for analysis."
      ],
      "metadata": {
        "id": "f9Hj7mUSOoLs"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}