This project is focused on predicting the future stock prices of Apple Inc. (AAPL) by integrating traditional financial time series data with sentiment analysis of news headlines. The approach enhances the predictive power by considering both historical trends and market sentiment, which often influences stock price movements.
This project predicts Apple (AAPL) stock prices by combining historical data with sentiment analysis of news headlines. The key features of the project include:
- Sentiment Analysis: Utilized NLTK for text processing and sentiment analysis on Times News Headlines to gauge market sentiment.
- Predictive Modeling: Integrated sentiment scores with historical stock prices to build a predictive model for AAPL's stock performance.
- Comprehensive Forecasting: Combined financial data with sentiment analysis for a holistic approach to stock price forecasting.
- Natural Language Processing (NLP): Leveraged NLTK for sentiment extraction from news headlines.
- Time Series Forecasting: Incorporated historical stock price data to enhance predictive accuracy.
- Sentiment-Driven Financial Modeling: Merged sentiment analysis with traditional stock price prediction methods.
- Python, NLTK, Pandas, Scikit-learn