Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments

In recent years, the intersection between financial market behavior and social media has emerged as a sought-after source of information, meeting the requirements of investors, institutions, regulators, researchers, and policymakers. Assessing sentiment and emotions aids in evaluating public psychology on particular stocks, assets, or the overall market, with shifts often aligning with market movements. Previously, machine learning, both traditional and deep learning methods, targeted discerning stock market sentiment and emotion without conducting studies to offer comprehensive explanations for these behavioral factors. In this study, we introduce a multitasking sequence-to-sequence model that integrates financial investment analysis with sentiment and emotion analysis from tweets upheld by an explanation mechanism. We also present the FinEMA dataset, featuring sentiment, emotion, and cause labels on financial stock market changes. Our study highlights how joint learning improves performance in discerning sentiment and emotion by utilizing interrelated features, enhancing task effectiveness. Our proposed model, the Emotion-Sentiment Attention Network (ESAN), achieved 89% accuracy in sentiment identification and 79% accuracy in emotion recognition, outperforming conventional machine learning methods. Furthermore, our findings indicate a positive outlook for the stock market in the latter half of 2023, which has intensified investor optimism, though some individuals still harbor uncertainties. Conclusively, our results suggest that regenerating existing computational tools can open up new research opportunities to address relevant novel tasks. The primary aim of this study is to elucidate the diverse dimensions of financial market behaviour and offer explanatory insights for the research community. The authors maintain impartiality towards specific stocks. It’s essential to note that stock market investments inherently carry market risks and potential losses. The market information within the research findings remains independent of the authors’ viewpoints.