Predicting Stock Prices Using Sentiment Analysis of News

Introduction

Predicting stock prices is a complex task influenced by numerous factors, including market trends, economic indicators, and public sentiment. This project proposal outlines a system that utilizes sentiment analysis of news articles to predict stock price movements. By analyzing the sentiment expressed in financial news, the system aims to enhance the accuracy of stock price predictions.

Background

Recent research has shown that sentiment analysis can significantly impact stock price prediction models. Sentiment analysis involves extracting and quantifying emotions or opinions from text data, such as news articles. Studies have demonstrated a strong correlation between news sentiment and stock price fluctuations, suggesting that positive news can drive prices up while negative news can lead to declines.

Project Objective

The primary objective of this project is to develop a robust model that predicts stock price movements by analyzing the sentiment of financial news articles. This model will leverage machine learning techniques to classify news sentiment and correlate it with historical stock prices.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Kaggle Stock Sentiment Analysis dataset for training and evaluation.
• Text Preprocessing: Clean and preprocess text data by removing stop words, stemming, and tokenization.

2. Sentiment Analysis

• Sentiment Classification: Implement sentiment analysis using Natural Language Processing (NLP) techniques to classify news articles into positive, negative, or neutral categories.
• Sentiment Scoring: Assign sentiment scores to each article based on the classification results.

3. Model Development

• Machine Learning Models: Develop models such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to predict stock prices based on sentiment scores and historical data.
• Feature Engineering: Incorporate additional features such as historical stock prices, trading volumes, and technical indicators.

4. Training and Evaluation

• Training: Train the models using historical data and sentiment scores with cross-validation techniques.
• Evaluation Metrics: Measure performance using metrics such as accuracy, precision, recall, F1-score, and Mean Absolute Error (MAE).

Expected Outcomes

The proposed system is expected to provide more accurate stock price predictions by integrating sentiment analysis with traditional financial indicators. By understanding the impact of news sentiment on market movements, the system should offer valuable insights for investors and traders.

Conclusion

This project aims to advance the field of stock price prediction by incorporating sentiment analysis of financial news articles. The integration of NLP techniques with machine learning models is anticipated to improve prediction accuracy and provide a competitive edge in financial decision-making.

For further details on related research, please refer to the paper "Predicting Stock Prices Using Sentiment Analysis of News," available at ScienceDirect.