Understanding market sentiment analysis: A survey
这篇综述梳理了近四十年市场情绪分析的方法演进，从基于词典到机器学习和深度学习，并用网络分析揭示经济学和金融学中情绪研究的关键趋势与跨学科合作方向。
Abstract 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.