A Critical Examination of AI Driven Sentiment Analysis Integration in Stock Market Prediction
DOI:
https://doi.org/10.66592/pykmby16Keywords:
Sentiment analysis, stock market prediction, artificial intelligence, natural language processing, behavioral finance, machine learning, social media analyticsAbstract
The integration of artificial intelligence driven sentiment analysis with traditional stock market prediction models has emerged as a promising yet methodologically contested frontier in computational finance. This paper critically examines the theoretical foundations, empirical validity, and practical limitations of combining natural language processing of social media and news data with quantitative forecasting frameworks. Drawing upon a systematic synthesis of scholarship from 2019 to 2026, we identify a fundamental tension between the high dimensionality, noise prone nature of sentiment signals and the precision demands of financial forecasting. Through a theoretical methodological approach grounded in behavioral finance and machine learning theory, we propose a conceptual framework for evaluating sentiment integration strategies based on signal to noise ratios, temporal alignment, and source credibility weighting. Our findings indicate that while sentiment analysis offers marginal improvements in prediction accuracy for highly volatile, event driven market conditions, its generalizable utility remains constrained by challenges of context dependency, algorithmic opacity, and the efficient market hypothesis counterargument. Notably, the paper demonstrates that much of the claimed predictive power attributed to sentiment integration may be explained by post hoc rationalization rather than genuine forward looking information content. We conclude by proposing a hybrid evaluation protocol and outlining research priorities for improving sentiment based feature engineering, particularly regarding domain adaptive transformer models and cross platform sentiment validation.
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