DETERMINISTIC NEURAL LEARNING WITH SENTIMENT INTELLIGENCE FOR FINANCIAL MARKET FORECASTING

Abstract

The growing availability of user-generated content across social networks, blogs, news portals, and multimedia platforms has fundamentally altered how financial markets can be studied and predicted. Investor sentiment, once regarded as an intangible psychological factor, has become measurable at unprecedented scale through sentiment analysis techniques applied to text and speech data. In recent years, this development has led to a convergence between natural language processing, machine learning, and financial forecasting. Traditional econometric models, while robust in handling structured numerical data, are ill-equipped to incorporate large-scale subjective opinion data, whereas modern machine learning approaches can process unstructured information but often lack stability and interpretability. Within this context, hybrid frameworks that integrate sentiment analysis with optimized neural learning systems have emerged as a promising avenue for stock market prediction. A particularly influential direction has been the combination of sentiment-extracted features with Extreme Learning Machines that are deterministically optimized to avoid the instability associated with random weight initialization, as demonstrated by Hebbar et al. (2025).

This research article develops a comprehensive theoretical and methodological synthesis of sentiment-based stock market prediction through deterministically optimized Extreme Learning Machines. Drawing strictly on the literature provided, it situates financial sentiment analysis within the broader evolution of opinion mining, surveys lexical, statistical, and deep learning approaches to sentiment extraction, and critically examines how these approaches can be aligned with advanced predictive architectures. The article conceptualizes stock markets as complex socio-technical systems in which human emotions, expectations, and collective narratives shape price movements alongside fundamental economic indicators. By integrating insights from text-based sentiment analysis, speech-based sentiment analysis, and machine learning classifiers such as Naive Bayes, Support Vector Machines, Maximum Entropy models, and deep neural networks, this study elaborates a unified framework for market forecasting.

The methodology proposed is text-based and conceptual, emphasizing deterministic optimization of Extreme Learning Machines as a way to stabilize learning, improve generalization, and increase the reliability of sentiment-driven predictions. Results are interpreted through the lens of existing empirical findings reported in the literature, especially those linking sentiment polarity to market movements. The discussion extends these findings by exploring theoretical implications, limitations, and future research directions. By synthesizing diverse strands of sentiment analysis research with financial prediction modeling, this work offers a rigorous academic foundation for understanding how collective emotion, when captured through computational methods, can be transformed into actionable market intelligence.