@inproceedings{db7eabd47a794934837f9c897f6ab994,
title = "Credibility estimation of stock comments based on publisher and information uncertainty evaluation",
abstract = "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.",
keywords = "Credibility estimation, Information source credibility, Information uncertainty",
author = "Qiaoyun Qiu and Ruifeng Xu and Bin Liu and Lin Gui and Yu Zhou",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",
year = "2014",
doi = "10.1007/978-3-662-45652-1_40",
language = "English",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "400--408",
editor = "Xizhao Wang and Qiang He and Chan, {Patrick P.K.} and Witold Pedrycz",
booktitle = "Machine Learning and Cybernetics - 13th International Conference, Proceedings",
address = "Germany",
}