Investor Sentiment and Market Dynamics : A Bibliometric Analysis of Behavioral Finance Research in the Digital Era

Abstract

This study presents a bibliometric analysis of global research on investor sentiment, a central construct in behavioral finance that examines how the collective psychology of market participants shapes asset prices and market dynamics. Drawing on 428 documents indexed in the Scopus database, the analysis employs VOSviewer to construct keyword co-occurrence, overlay, and density visualizations that map the intellectual structure, temporal evolution, and research intensity of the field. The findings show that investor sentiment and behavioral finance form the conceptual core of the literature, closely tied to established themes such as stock market behavior, market efficiency, volatility, and overconfidence. The overlay analysis reveals a marked thematic shift in recent years toward computational and data-driven approaches, including machine learning, sentiment analysis, natural language processing, and deep learning, reflecting the growing influence of digital technologies and social media data on sentiment measurement. Density analysis confirms that investor sentiment and behavioral finance remain the most intensively researched constructs, while machine-learning-based sentiment analysis constitutes an emerging frontier. Citation analysis further identifies the most influential publications shaping the field's development. Collectively, these findings provide a comprehensive map of investor sentiment research and highlight promising directions for future inquiry at the intersection of behavioral finance and digital analytics.