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📈 Stock Sentiment Analysis and Price Prediction

This project focuses on predicting stock prices using sentiment analysis and statistical/machine learning models like SARIMAX, LSTM, and Linear Regression. It combines historical stock price data with sentiment data to forecast stock price trends effectively.

🚀 Features

  • Sentiment Analysis: Analyzes text-based data to derive sentiment scores.
  • Stock Price Forecasting:
    • ARIMA/SARIMAX for time-series prediction.
    • LSTM (Long Short-Term Memory) for deep learning-based forecasting.
    • Linear Regression for quick predictions.
  • Visualization: Graphs for actual vs predicted stock prices.
  • Comparison: Compare results of different forecasting models (ARIMA, LSTM, Linear Regression).

Models Used

1. Sentiment Analysis

- Sentiment scores extracted from external data sources (e.g., news headlines, social media).
- Scores are used as features for price predictions.

2. Time Series Models

  - ARIMA: For statistical forecasting based on stock price trends.
  - LSTM: A deep learning model ideal for sequential data like time series.

3. Linear Regression

   - A baseline regression model predicting future stock prices.

🛠️ Tech Stack

  • Python 3.11 (Required)
  • Pandas: Data manipulation and analysis.
  • NumPy: Numerical computations.
  • Scikit-learn: Machine learning algorithms.
  • TensorFlow/Keras: Deep learning for LSTM models.
  • Statsmodels: ARIMA and SARIMAX models.
  • Matplotlib: Data visualization.
  • NLTK or TextBlob: Sentiment analysis.

⚙️ Installation

To set up and run the project, follow these steps:

1. Clone the Repository

git clone https://github.com/your-username/stock-sentiment-analysis.git
cd stock-sentiment-analysis

2. Create a Virtual Environment

python -m venv env  
source env/bin/activate      # On Windows: env\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

🚀 Usage

Prepare Your Dataset

  • Place historical stock prices (CSV) in the data/ folder.

⚙️ Configuration

You can modify key parameters in the main script:

  • Split Size: Ratio of training/testing data.
  • Forecast Days: Number of future days to predict.
  • Ticker Symbol: Stock symbol to analyze (e.g., META, AAPL).

Run the Project

To execute the analysis and predictions, use the following command:

python main.py

📊 Output

  • Graphs: Actual vs predicted stock price graphs are saved under the results/graphs/ directory.
  • Forecasts: Future price predictions are displayed in the console.

Sample:

Tomorrow's META Closing Price Prediction: 340.25  
Linear Regression RMSE: 5.72  
ARIMA RMSE: 6.11  
LSTM RMSE: 4.89

✅ Dependencies

  • Python 3.11
  • Libraries:
    • pandas
    • numpy
    • matplotlib
    • sklearn
    • tensorflow
    • statsmodels

📚 Future Improvements

  • Integrate real-time sentiment analysis using live news APIs.
  • Add support for additional stock market models.
  • Optimize hyperparameters for LSTM and ARIMA.

👨‍💻 Contact

⚖️ License

This project is licensed under the MIT License.

Feel free to adapt it further! Let me know if you need tweaks or additions. 🚀