stock_sentimental.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                Text  Sentiment\n",
      "0  Kickers on my watchlist XIDE TIT SOQ PNK CPW B...          1\n",
      "1  user: AAP MOVIE. 55% return for the FEA/GEED i...          1\n",
      "2  user I'd be afraid to short AMZN - they are lo...          1\n",
      "3                                  MNTA Over 12.00            1\n",
      "4                                   OI  Over 21.37            1\n",
      "Text         0\n",
      "Sentiment    0\n",
      "dtype: int64\n",
      "(4632, 34) (4632,)\n",
      "(1159, 34) (1159,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# Load dataset\n",
    "data = pd.read_csv('Stock_data.csv')\n",
    "\n",
    "# Display first few rows of the dataset\n",
    "print(data.head())\n",
    "\n",
    "# Check for missing values\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Drop missing values if any\n",
    "data = data.dropna()\n",
    "\n",
    "# Encode sentiment labels (assuming they are in a column named 'Sentiment')\n",
    "le = LabelEncoder()\n",
    "data['Sentiment'] = le.fit_transform(data['Sentiment'])\n",
    "\n",
    "# Split dataset into features and labels\n",
    "X = data['Text']\n",
    "y = data['Sentiment']\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Tokenization\n",
    "tokenizer = Tokenizer(num_words=5000)  # Consider top 5000 words\n",
    "tokenizer.fit_on_texts(X_train)\n",
    "\n",
    "# Convert texts to sequences\n",
    "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
    "X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
    "\n",
    "# Pad sequences to ensure uniform input size\n",
    "max_length = max(len(x) for x in X_train_seq)\n",
    "X_train_pad = pad_sequences(X_train_seq, maxlen=max_length, padding='post')\n",
    "X_test_pad = pad_sequences(X_test_seq, maxlen=max_length, padding='post')\n",
    "\n",
    "# Display shapes of data\n",
    "print(X_train_pad.shape, y_train.shape)\n",
    "print(X_test_pad.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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\"</span>\n",
       "</pre>\n"
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       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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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\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\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",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\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_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\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"
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\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",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "\n",
    "# Define LSTM model\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=5000, output_dim=128))  # Embedding layer\n",
    "model.add(LSTM(128, return_sequences=True))  # LSTM layer\n",
    "model.add(Dropout(0.5))  # Dropout to prevent overfitting\n",
    "model.add(LSTM(64))  # Second LSTM layer\n",
    "model.add(Dropout(0.5))  # Dropout\n",
    "model.add(Dense(1, activation='sigmoid'))  # Output layer for binary classification\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Display model summary\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 73ms/step - accuracy: 0.6265 - loss: 0.6656 - val_accuracy: 0.6343 - val_loss: 0.6423\n",
      "Epoch 2/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.7415 - loss: 0.5434 - val_accuracy: 0.7573 - val_loss: 0.4971\n",
      "Epoch 3/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 59ms/step - accuracy: 0.8876 - loss: 0.3093 - val_accuracy: 0.7616 - val_loss: 0.5779\n",
      "Epoch 4/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.9329 - loss: 0.1984 - val_accuracy: 0.7519 - val_loss: 0.6860\n",
      "Epoch 5/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - accuracy: 0.9661 - loss: 0.1211 - val_accuracy: 0.7454 - val_loss: 0.7584\n",
      "Epoch 6/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - accuracy: 0.9642 - loss: 0.1280 - val_accuracy: 0.7357 - val_loss: 0.8707\n",
      "Epoch 7/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 60ms/step - accuracy: 0.9692 - loss: 0.1043 - val_accuracy: 0.7195 - val_loss: 0.8572\n",
      "Epoch 8/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.9730 - loss: 0.0922 - val_accuracy: 0.7335 - val_loss: 0.8631\n",
      "Epoch 9/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.9842 - loss: 0.0609 - val_accuracy: 0.7357 - val_loss: 1.1649\n",
      "Epoch 10/10\n",
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 73ms/step - accuracy: 0.9794 - loss: 0.0758 - val_accuracy: 0.7228 - val_loss: 1.1020\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train_pad, y_train, epochs=10, batch_size=64, validation_split=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.7369 - loss: 1.0339\n",
      "Test Accuracy: 74.46%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test_pad, y_test)\n",
    "print(f'Test Accuracy: {accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 500ms/step\n",
      "Sentiment: Positive\n"
     ]
    }
   ],
   "source": [
    "def predict_sentiment(text):\n",
    "    sequence = tokenizer.texts_to_sequences([text])\n",
    "    padded = pad_sequences(sequence, maxlen=max_length, padding='post')\n",
    "    prediction = model.predict(padded)\n",
    "    # Assuming binary classification: 0 for Negative, 1 for Positive\n",
    "    if prediction[0] > 0.5:\n",
    "        print(\"Sentiment: Positive\")\n",
    "    else:\n",
    "        print(\"Sentiment: Negative\")\n",
    "\n",
    "# Example usage\n",
    "new_text = \"The stock market is  bad today.\"\n",
    "predict_sentiment(new_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('sentiment_model.keras')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: \"The stock market is performing well today.\"\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "Sentiment: Positive\n",
      "Text: \"The stock market is performing bad today.\"\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "Sentiment: Positive\n",
      "Text: \"I'm very happy with the profits I've made.\"\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "Sentiment: Positive\n",
      "Text: \"I'm disappointed with the losses this quarter.\"\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "Sentiment: Negative\n",
      "Text: \"It's a great time to invest in stocks!\"\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "Sentiment: Positive\n"
     ]
    }
   ],
   "source": [
    "test_texts = [\n",
    "    \"The stock market is performing well today.\",\n",
    "    \"The stock market is performing bad today.\",\n",
    "    \"I'm very happy with the profits I've made.\",\n",
    "    \"I'm disappointed with the losses this quarter.\",\n",
    "    \"It's a great time to invest in stocks!\"\n",
    "    \n",
    "]\n",
    "\n",
    "for text in test_texts:\n",
    "    print(f'Text: \"{text}\"')\n",
    "    predict_sentiment(text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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