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BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights

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  • Enmin Zhu
  • Jerome Yen

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.

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  • Enmin Zhu & Jerome Yen, 2024. "BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights," Papers 2404.02053, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2404.02053
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    1. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
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