BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights

TL;DR

This paper demonstrates that using BERTopic for sentiment analysis of stock market comments improves the accuracy of stock price prediction models by providing valuable insights into market sentiment and volatility.

Contribution

It introduces a novel integration of BERTopic-based sentiment analysis with deep learning models for enhanced stock market prediction.

Findings

Sentiment analysis with BERTopic improves prediction accuracy.

Topic-based sentiment provides insights into market volatility.

Enhanced models outperform traditional methods.

Abstract

This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors.