lstm_sentiment_analysis.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Anil951/YouTube-Video-Recommendation-for-Domain-Specific-Content/blob/main/lstm_sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*sentiment analysis using LSTM*"
      ],
      "metadata": {
        "id": "E4kDJrHzs957"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load your dataset\n",
        "file_path = r\"result.csv\"  # Replace with the path to your dataset\n",
        "df = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "Vgzt_7lNG72J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def decontract(text):\n",
        "    text = re.sub(r\"won\\'t\", \"will not\", text)\n",
        "    text = re.sub(r\"can\\'t\", \"can not\", text)\n",
        "    text = re.sub(r\"n\\'t\", \" not\", text)\n",
        "    text = re.sub(r\"\\'re\", \" are\", text)\n",
        "    text = re.sub(r\"\\'s\", \" is\", text)\n",
        "    text = re.sub(r\"\\'d\", \" would\", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will\", text)\n",
        "    text = re.sub(r\"\\'t\", \" not\", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have\", text)\n",
        "    text = re.sub(r\"\\'m\", \" am\", text)\n",
        "    return text\n",
        "\n",
        "#set of custom stop words\n",
        "stop_words= set(['br', 'the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
        "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
        "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
        "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
        "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
        "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
        "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
        "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
        "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
        "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
        "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
        "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
        "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
        "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
        "            'won', \"won't\", 'wouldn', \"wouldn't\"])\n",
        "\n",
        "lemmatizer = nltk.WordNetLemmatizer()\n",
        "\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "\n",
        "def preprocess_text(review):\n",
        "    review = re.sub(r\"http\\S+\", \"\", review)             # removing website links\n",
        "    review = BeautifulSoup(review, 'lxml').get_text()   # removing html tags\n",
        "    review = decontract(review)                         # decontracting\n",
        "    review = re.sub(\"\\S*\\d\\S*\", \"\", review).strip()     # removing the words with numeric digits\n",
        "    review = re.sub('[^A-Za-z]+', ' ', review)          # removing non-word characters\n",
        "    review = review.lower()                             # converting to lower case\n",
        "    review = [word for word in review.split(\" \") if not word in stop_words] # removing stop words\n",
        "    review = [lemmatizer.lemmatize(token, \"v\") for token in review] #Lemmatization\n",
        "    review = \" \".join(review)\n",
        "    review.strip()\n",
        "    return review"
      ],
      "metadata": {
        "id": "nOeNwK8yS9LZ",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "1af69497-8a39-49b4-f52a-8e04c1bc373c"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['up_text'] = df['text'].apply(lambda x: preprocess_text(x))\n",
        "df"
      ],
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      "execution_count": null,
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              "                                                   text  sentiment  \\\n",
              "0                                        Love you sir!!          1   \n",
              "1     Another great explanation by Abdul sir. Thank ...          1   \n",
              "2     I had no idea what was going on in the first o...          1   \n",
              "3                                         great job!!!!          1   \n",
              "4                                      Thank you Sir !!          1   \n",
              "...                                                 ...        ...   \n",
              "4492  I got a little confused after watching this vi...         -1   \n",
              "4493  I got 18 points... I think most of the wrong a...         -1   \n",
              "4494  Why is 3a in the first block correct? A lingui...         -1   \n",
              "4495  Sometimes I feel like my English has really ad...         -1   \n",
              "4496  Thanks for exercise.  I need to listen much mo...         -1   \n",
              "\n",
              "                                                up_text  \n",
              "0                                             love sir   \n",
              "1       another great explanation abdul sir thank much   \n",
              "2            no idea go first one one clear everything   \n",
              "3                                            great job   \n",
              "4                                            thank sir   \n",
              "...                                                 ...  \n",
              "4492  get little confuse watch video exactly mean ba...  \n",
              "4493           get point think wrong answer forget say   \n",
              "4494  first block correct linguistic major refer lan...  \n",
              "4495  sometimes feel like english really advance eve...  \n",
              "4496         thank exercise need listen much bad score   \n",
              "\n",
              "[4497 rows x 3 columns]"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dense, SpatialDropout1D\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Assuming df is your DataFrame with 'text' and 'sentiment' columns\n",
        "\n",
        "max_fatures = 2000\n",
        "tokenizer = Tokenizer(num_words=max_fatures, split=' ')\n",
        "tokenizer.fit_on_texts(df['up_text'].values)\n",
        "X = tokenizer.texts_to_sequences(df['up_text'].values)\n",
        "X = pad_sequences(X)\n",
        "\n",
        "embed_dim = 128\n",
        "lstm_out = 100\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_fatures, embed_dim, input_length=X.shape[1]))\n",
        "model.add(SpatialDropout1D(0.4))\n",
        "model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))  # Use 'sigmoid' for binary classification\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "\n",
        "Y = df['sentiment'].values\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n",
        "print(X_train.shape, Y_train.shape)\n",
        "print(X_test.shape, Y_test.shape)\n",
        "\n",
        "\n",
        "model.fit(X_train, Y_train, epochs=7, verbose=2, validation_data=(X_test, Y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1boMd2R-qSN",
        "outputId": "4d3ea796-0c25-4878-fd31-a29e1588532a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 234, 128)          256000    \n",
            "                                                                 \n",
            " spatial_dropout1d (Spatial  (None, 234, 128)          0         \n",
            " Dropout1D)                                                      \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 196)               254800    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 197       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 510997 (1.95 MB)\n",
            "Trainable params: 510997 (1.95 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n",
            "(3012, 234) (3012,)\n",
            "(1485, 234) (1485,)\n",
            "Epoch 1/7\n",
            "95/95 - 147s - loss: 0.3843 - accuracy: 0.9336 - val_loss: 0.3655 - val_accuracy: 0.9340 - 147s/epoch - 2s/step\n",
            "Epoch 2/7\n",
            "95/95 - 145s - loss: 0.1988 - accuracy: 0.9220 - val_loss: 0.2063 - val_accuracy: 0.9138 - 145s/epoch - 2s/step\n",
            "Epoch 3/7\n",
            "95/95 - 143s - loss: -3.2862e-02 - accuracy: 0.9021 - val_loss: 0.1286 - val_accuracy: 0.8976 - 143s/epoch - 2s/step\n",
            "Epoch 4/7\n",
            "95/95 - 146s - loss: -4.8724e-01 - accuracy: 0.9024 - val_loss: -3.0928e-02 - val_accuracy: 0.8714 - 146s/epoch - 2s/step\n",
            "Epoch 5/7\n",
            "95/95 - 141s - loss: -1.2952e+00 - accuracy: 0.9084 - val_loss: -2.4146e-01 - val_accuracy: 0.8936 - 141s/epoch - 1s/step\n",
            "Epoch 6/7\n",
            "95/95 - 141s - loss: -2.0265e+00 - accuracy: 0.9087 - val_loss: 0.1164 - val_accuracy: 0.8862 - 141s/epoch - 1s/step\n",
            "Epoch 7/7\n",
            "95/95 - 140s - loss: -1.0663e+00 - accuracy: 0.9200 - val_loss: 0.0253 - val_accuracy: 0.8680 - 140s/epoch - 1s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a740a0c3010>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_sentiment(model, tokenizer, text):\n",
        "    # Tokenize and pad the input text\n",
        "    sequences = tokenizer.texts_to_sequences([text])\n",
        "    padded_sequences = pad_sequences(sequences, maxlen=model.input_shape[1])\n",
        "\n",
        "    # Make predictions\n",
        "    probability = model.predict(padded_sequences)[0]\n",
        "\n",
        "    # Convert probability to sentiment label\n",
        "    sentiment_label = \"positive\" if probability > 0.5 else \"negative\"\n",
        "\n",
        "    # Convert sentiment label to target value\n",
        "    target_value = 1 if sentiment_label == \"positive\" else -1\n",
        "\n",
        "    return sentiment_label, target_value, probability\n",
        "\n",
        "# Example usage:\n",
        "new_text = \"this is a bad video\"\n",
        "sentiment, target_value, probability = predict_sentiment(model, tokenizer, new_text)\n",
        "\n",
        "print(f\"Predicted Sentiment: {sentiment}\")\n",
        "print(f\"Predicted Target Value: {target_value}\")\n",
        "print(f\"Probability: {probability}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u73LTGDgWpTM",
        "outputId": "c82ac20d-a034-4409-ec83-3c52de0b6c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 149ms/step\n",
            "Predicted Sentiment: negative\n",
            "Predicted Target Value: -1\n",
            "Probability: [3.5566947e-18]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model to the Colab file system\n",
        "model.save(\"sentiment_model.h5\")\n",
        "\n",
        "# Save the tokenizer\n",
        "with open(\"tokenizer.pkl\", \"wb\") as tokenizer_file:\n",
        "    pickle.dump(tokenizer, tokenizer_file)\n",
        "\n",
        "# Download the saved files to your local machine\n",
        "from google.colab import files\n",
        "\n",
        "files.download(\"sentiment_model.h5\")\n",
        "files.download(\"tokenizer.pkl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "8HvqIjyhjQQc",
        "outputId": "f6326d83-f35a-48dc-ac4a-b5aec54fccf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_55bfd0b2-0b4e-44e8-8274-73eda73d368c\", \"sentiment_model.h5\", 6172008)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_fb9a846b-694b-4057-ac8b-28ab78c0efb2\", \"tokenizer.pkl\", 184123)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "import pickle\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the saved model\n",
        "loaded_model = load_model(\"/content/drive/MyDrive/yt/sentiment_model.h5\")\n",
        "\n",
        "# Load the tokenizer\n",
        "with open(\"/content/drive/MyDrive/yt/tokenizer.pkl\", \"rb\") as tokenizer_file:\n",
        "    loaded_tokenizer = pickle.load(tokenizer_file)\n",
        "\n",
        "def predict_sentiment(model, tokenizer, text):\n",
        "    # Tokenize and pad the input text\n",
        "    sequences = tokenizer.texts_to_sequences([text])\n",
        "    padded_sequences = pad_sequences(sequences, maxlen=model.input_shape[1])\n",
        "\n",
        "    # Make predictions\n",
        "    probability = model.predict(padded_sequences)[0]\n",
        "\n",
        "    # Convert probability to sentiment label\n",
        "    sentiment_label = \"positive\" if probability > 0.5 else \"negative\"\n",
        "\n",
        "    # Convert sentiment label to target value\n",
        "    target_value = 1 if sentiment_label == \"positive\" else -1\n",
        "\n",
        "    return sentiment_label, target_value, probability\n",
        "\n",
        "# Example usage:\n",
        "new_text = \"nice video\"\n",
        "sentiment, target_value, probability = predict_sentiment(loaded_model, loaded_tokenizer, new_text)\n",
        "\n",
        "print(f\"Predicted Sentiment: {sentiment}\")\n",
        "print(f\"Predicted Target Value: {target_value}\")\n",
        "print(f\"Probability: {probability}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KALA82pIlGnt",
        "outputId": "2857952e-4e57-45eb-f738-f86c33c56280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 20 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7a7461ce65f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 386ms/step\n",
            "Predicted Sentiment: positive\n",
            "Predicted Target Value: 1\n",
            "Probability: [0.98200536]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}