Evaluating NLP Innovations for Short-Term Financial Forecasting

Abstract

Rapid information diffusion through social media and digital news platforms has intensified interest in using natural language processing (NLP)–based sentiment analysis for short-term financial forecasting. This paper evaluates the effectiveness of sentiment-driven models in predicting near-term stock price movements, with particular attention to retail-investor-dominated environments. Drawing on prior empirical research and case studies such as GameStop and Opendoor Technologies, the study examines how sentiment extracted from Twitter, Reddit, and financial news outlets correlates with intraday volatility and abnormal returns. The performance of lexicon-based, classical machine learning, and deep learning (LSTM/GRU) approaches is compared in terms of predictive accuracy, interpretability, and practical limitations. While news-based sentiment generally exhibits greater reliability than social media sentiment due to its structured nature, overall predictive power remains modest and highly sensitive to data quality, noise, and temporal instability. The findings indicate that sentiment analysis captures short-lived behavioral signals rather than durable price trends, limiting its standalone usefulness for profitable trading. The paper concludes that sentiment-based NLP models are best employed as complementary tools alongside traditional financial indicators, rather than as independent forecasting mechanisms, and highlights directions for future research focused on robustness, interpretability, and hybrid modeling frameworks.

Keywords

Convergence Science - Social Science

Citation

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