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Understanding investor emotions has emerged as a pivotal factor in financial sentiment analysis and stock market prediction, offering deeper insights into market dynamics beyond traditional numerical data. This article delves into the integration of nuanced investor emotions into financial analytics, underscoring their significant role in shaping market behavior and investment decisions. To facilitate this, we developed a comprehensive dataset, meticulously curated from StockTwits, a prominent financial social media platform. This dataset encompasses over 10,000 English comments, each annotated with both binary financial sentiments — bullish and bearish — and twelve fine-grained emotion categories inspired by behavioral finance and psychological theories. Our annotation pipeline employs a sophisticated multi-step process that synergizes human expertise with advanced pre-trained language models, ensuring high-quality and contextually accurate emotional labels.

Leveraging this dataset, we implemented a series of advanced machine learning models to perform financial sentiment classification and multivariate time series forecasting. Our experiments reveal that emotion-enhanced models, particularly those utilizing DistilBERT, significantly outperform traditional baselines in sentiment classification, achieving an average F1-score of 0.81. Furthermore, in predicting the S&P 500 index, models incorporating emotional features alongside textual and numerical data demonstrated superior accuracy, evidenced by a notable reduction in Mean Squared Error (MSE). These findings highlight the profound impact of investor emotions on market predictions, suggesting that incorporating emotional intelligence into financial models can enhance forecasting accuracy and provide a more holistic understanding of market trends.

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