11/7/2023 0 Comments Google trends sentiment analysis![]() However, there is a large amount of noise in text data and a massive workload of labelling investor sentiment. ![]() In finance research, studies on investor sentiment are carried out by selecting proxy indicators such as volatility index (VIX), initial public offering (IPO) number, etc., but fail to make use of a large number of text data generated in the interaction of investors in the stock bulletin board system (BBS), which reflects investor sentiment and affect the final investment decision to some extent ( Zhang and Zhang, 2021, pp.143-150). Third, there is an asymmetric effect of investor sentiment on excess return. Second, different themes have different degrees of influence, with “broad market sentiment” affecting the short term, corporate development involving a long time, and “corporate dividends” affecting both. First, investor sentiment classified by theme is positively correlated with excess return. An attempt is made to explore the correlation with stock excess return. Natural language processing and word-splitting tools empirically analyze text data from a well-known financial and stock information website. This paper uses the primary model for the unsupervised learning total probability generative model LDA (Latent Dirichlet Allocation). Unsupervised learning avoids the subjective human selection of data while reducing the workload. In recent years, text mining, machine learning and sentiment analysis have been very fruitful, and researchers can extract valuable information from social platforms more promptly and accurately. ![]() Investor sentiment has always been an active research topic in finance. 2Riphah School of Business and Management, Riphah International University, Lahore, Pakistan.1School of International Business, Guangdong University of Finance and Economics, Guangzhou, China.Meilan Chen 1 Zhiying Guo 1 Kashif Abbass 2 Wenfeng Huang 1*
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