金融市场文本情绪研究综述

Review of Textual Sentiment Research in Financial Markets

[目的]通过文献梳理分析和总结金融市场文本情绪发展现状,为后续研究提供参考。[文献范围]以“金融市场”“文本情感分析”“文本情绪”“投资者情绪”等以及相应英文为关键词在中国知网、Web of Science、谷歌学术等学术平台进行检索,延伸阅读相关文献,共筛选115篇文献进行综述。[方法]根据金融文本数据类型对提取的文本情绪分类,介绍文本情感分析框架,最后梳理文本情绪对金融市场影响的相关研究成果。[结果]金融文本情绪分为信息报告情绪、新闻媒体情绪和社交媒体情绪三种,在构造情绪指标时,应用较多的分析方法有基于词典的方法和基于机器学习的方法,三种文本情绪都对金融市场产生了一定的影响。[局限]筛选文本情感分析框架相关文献时,由于文本分析方法在各领域具有一定通用性,这类文献不完全聚焦于金融市场。[结论]在构建金融文本情绪指标时,应根据文本特点、研究条件、研究目标等的不同选择合适的情感分析方法。

[Objective]This paper analyzes and summarizes the current situation of the development of text sentiment in financial markets,and provides reference for subsequent related research.[Coverage]We used“financial market”,“text sentiment analysis”,“text sentiment”and“investor sentiment”as keywords to search on academic platforms such as CNKI,Web of Science and Google Academic,and extended the search for relevant literatures.A total of 115 papers were reviewed.[Methods]We classified the extracted text sentiment according to the type of the source financial text,then introduced the framework of text sentiment analysis,and finally sorted out the relevant research results on the impact of text sentiment on the financial markets.[Results]Text sentiment in financial markets can be divided into information reporting sentiment,news media sentiment and social media sentiment.In the construction of sentiment indicators,dictionary-based methods and machine learning-based methods are widely used.The above three text sentiments have a certain impact on the financial markets.[Limitations]Due to the universality of text analysis methods in various fields,the selected literature on the framework of text sentiment analysis is not entirely focused on the financial markets.[Conclusions]When constructing financial text sentiment indicators,we should choose the appropriate sentiment analysis method according to the text characteristics,research conditions and research objectives.