lstm_sentiment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1MvyF7QweGzy_eTKgxVhey5-FOCsKnq-q",
      "authorship_tag": "ABX9TyPvoyIBOXyJcdaFluaAgQoJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/efeecllk/investor-sentiments/blob/main/lstm_sentiment.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": "btWRGbIgeUmZ",
        "outputId": "b0df7262-7d28-4480-a337-785ac3460626"
      },
      "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 stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt_tab')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and Preprocess Dataset\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/NLP/clean_tweet.csv')\n",
        "df_label_0 = df[df['label'] == 0].sample(n=4000, random_state=42)\n",
        "df_label_1 = df[df['label'] == 1].sample(n=4000, random_state=42)\n",
        "df_label_2 = df[df['label'] == 2].sample(n=4000, random_state=42)\n",
        "\n",
        "df = pd.concat([df_label_0, df_label_1, df_label_2]).reset_index(drop=True)\n",
        "\n",
        "print(df['label'].value_counts())\n",
        "\n",
        "# Check dataset\n",
        "print(df.head())\n",
        "\n",
        "# Preprocessing function\n",
        "def preprocess_text(text):\n",
        "\n",
        "    tokens = word_tokenize(text)\n",
        "    tokens = [word for word in tokens if word not in stopwords.words('english')]\n",
        "    return ' '.join(tokens)\n",
        "\n",
        "df['clean_text'] = df['text_v'].apply(preprocess_text)\n",
        "\n",
        "# Encode sentiment labels\n",
        "label_encoder = LabelEncoder()\n",
        "df['sentiment_label'] = label_encoder.fit_transform(df['label'])  # 0: Neutral, 1: Bullish, 2: Bearish\n",
        "\n",
        "# Train-test split\n",
        "X = df['clean_text']\n",
        "y = df['sentiment_label']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "print(\"✅ Dataset is ready for modeling!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnsgz3FhexVZ",
        "outputId": "ab3be35c-a8f3-4d23-c06d-f043d9fee262"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label\n",
            "0    4000\n",
            "1    4000\n",
            "2    4000\n",
            "Name: count, dtype: int64\n",
            "                                              text_v  label\n",
            "0  financial expert predict steady increase value...      0\n",
            "1  coin experiencing surge trading volume today c...      0\n",
            "2  blackrock announces dividend payment dividend ...      0\n",
            "3  investor urged exercise caution face market fl...      0\n",
            "4  financial technology fintech rapidly transform...      0\n",
            "✅ Dataset is ready for modeling!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization and Padding\n",
        "\n",
        "tokenizer = Tokenizer(num_words=10000, oov_token='<OOV>')\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
        "X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "# Padding\n",
        "max_length = 50\n",
        "X_train_pad = pad_sequences(X_train_seq, maxlen=max_length, padding='post', truncating='post')\n",
        "X_test_pad = pad_sequences(X_test_seq, maxlen=max_length, padding='post', truncating='post')\n",
        "\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "print(f\"✅ Vocabulary Size: {vocab_size}\")\n",
        "print(f\"✅ Training data shape: {X_train_pad.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RH8d5uTqe3_7",
        "outputId": "a91b5b06-cfa0-4a37-97f1-5c6681361fe5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Vocabulary Size: 9803\n",
            "✅ Training data shape: (9600, 50)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build LSTM Model\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
        "\n",
        "lstm_model = Sequential([\n",
        "    Embedding(input_dim=vocab_size, output_dim=128, input_length=max_length),\n",
        "    LSTM(128, return_sequences=True),\n",
        "    LSTM(64),\n",
        "    Dropout(0.3),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "lstm_model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "lstm_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "uNCbgHt7gYLI",
        "outputId": "df75de97-07de-4855-c92d-fbe7fa9e0aa7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<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\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<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 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train LSTM Model\n",
        "\n",
        "history_lstm = lstm_model.fit(\n",
        "    X_train_pad, y_train,\n",
        "    validation_split=0.2,\n",
        "    epochs=5,\n",
        "    batch_size=32,\n",
        "    verbose=1\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTpQUcmDga9f",
        "outputId": "7c5cd1ff-891a-4236-a5ed-8566f065c7a7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 120ms/step - accuracy: 0.3182 - loss: 1.1022 - val_accuracy: 0.3297 - val_loss: 1.0989\n",
            "Epoch 2/5\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 121ms/step - accuracy: 0.4257 - loss: 1.0329 - val_accuracy: 0.7255 - val_loss: 0.6787\n",
            "Epoch 3/5\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 122ms/step - accuracy: 0.8026 - loss: 0.5588 - val_accuracy: 0.8094 - val_loss: 0.5543\n",
            "Epoch 4/5\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 121ms/step - accuracy: 0.8925 - loss: 0.3674 - val_accuracy: 0.8146 - val_loss: 0.5705\n",
            "Epoch 5/5\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 116ms/step - accuracy: 0.9267 - loss: 0.2865 - val_accuracy: 0.8250 - val_loss: 0.5370\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "loss, accuracy = lstm_model.evaluate(X_test_pad, y_test)\n",
        "print(f\"LSTM Test Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Plot accuracy\n",
        "plt.plot(history_lstm.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history_lstm.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('LSTM Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "6TEoOxX2gieM",
        "outputId": "e29b3498-c17b-4455-f479-6d8f260c79cb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 43ms/step - accuracy: 0.8290 - loss: 0.4924\n",
            "LSTM Test Accuracy: 0.83\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "lstm_model.save('/content/drive/MyDrive/NLP/lstm_model.h5')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgUZO8pSlCMa",
        "outputId": "97978dbb-ca8f-48a5-e2a2-b328fb488e16"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Performances\n",
        "results = {\n",
        "    'LSTM': lstm_model.evaluate(X_test_pad, y_test)[1],\n",
        "}\n",
        "print(results)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4SUSrwsiOPE",
        "outputId": "025eb461-382b-492d-c276-9daf1a975ecd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 30ms/step - accuracy: 0.8290 - loss: 0.4924\n",
            "{'LSTM': 0.8316666483879089}\n"
          ]
        }
      ]
    }
  ]
}