Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Market sentiment analysis (MSA) has evolved significantly over nearly four decades, growing in relevance and application in economics and finance. This paper extensively reviews MSA, encompassing methodologies ranging from lexicon‐based techniques to traditional Machine Learning (ML), Deep Learning (DL), and hybrid approaches. Emphasizing the transition from rudimentary word counters to sophisticated feature extraction from diverse sources such as news, social media, and share prices, the study presents an updated state‐of‐the‐art review of sentiment analysis. Furthermore, using network analysis, a bibliometric and scientometric lens is applied to map the expanding footprint of sentiment research within economics and finance, revealing key trends, dominant research hubs, and potential areas for interdisciplinary collaboration. This exploration consolidates the foundational and emerging methods in MSA and underscores its dynamic interplay with global financial ecosystems and the imperative for future integrative research trajectories.