Credibility estimation of stock comments based on publisher and information uncertainty evaluation

Recently, there are rapidly increasing stock-related comments sharing on Internet. However, the qualities of these comments are quite different. This paper presents an automatic approach to identify high quality stock comments by means of estimating the credibility of the comments from two aspects. Firstly, the credibility of information source is evaluated by estimating the historical credibility and industry-related credibility using a linear regression model. Secondly, the credibility of the comment information is estimated through calculating the uncertainty of comment content using an uncertainty glossary based matching method. The final stock comment credibility is obtained by incorporating the above two credibility measures. The experiments on real stock comment dataset show that the proposed approach identifies high quality stock comments and institutions/ individuals effectively.