Predicting Stock Market Performance Based on Sentiment Analysis of Online Comments

In China's retail-focused stock market, the influence of social media sentiment during off-hours on the next day's opening price has received limited attention. This paper takes Kweichow Moutai—a leading Chinese company with substantial market capitalization—as the research sample. It gathers investor commentary data from financial platforms, and uses natural language processing tools (SnowNLP) to develop a multidimensional sentiment index (including average sentiment score, positive ratio, and sentiment volatility). By integrating this index with stock trading data and macroeconomic indicators, this study designs a dual-channel LSTM model: one channel for market technical features (e.g., price, volume) and the other for sentiment features, aiming to analyze the impact of off-hours sentiment on opening prices. Empirical results indicate that overnight sentiment has significant predictive power for the next day's opening price; meanwhile, sentiment transmission is asymmetric, making predictions more challenging in declining markets. Additionally, high-frequency sentiment data significantly outperforms low-frequency data in market prediction accuracy. This research expands the understanding of how investor sentiment influences the market over time, providing practical insights for market participants to develop effective strategies and manage risks.