BERTopic驱动的股市预测：揭示情感洞察
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
AI总结：
本文提出利用BERTopic从股市评论中提取主题情感，并将其与深度学习模型结合以提升股票价格预测性能，验证了主题情感对市场波动和价格趋势的隐含价值。
摘要：
本文探讨了自然语言处理（NLP）与金融分析的交叉领域，重点关注情感分析在股票价格预测中的影响。我们采用BERTopic这一先进的NLP技术，分析从股票市场评论中提取的主题的情感。我们的方法将这种情感分析与各种深度学习模型相结合，这些模型以其在时间序列和股票预测任务中的有效性而闻名。通过全面的实验，我们证明融入主题情感显著提升了这些模型的性能。结果表明，股票市场评论中的主题为股市波动性和价格趋势提供了隐含且有价值的洞察。本研究通过展示NLP在丰富金融分析方面的潜力为该领域做出了贡献，并为进一步研究实时情感分析以及探索市场情感的情绪和语境方面开辟了途径。将BERTopic等先进NLP技术与传统金融分析方法相结合，标志着在开发更复杂的工具以理解和预测市场行为方面迈出了一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
