Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction

被引：0

作者：

Thien Hai Nguyen ^{[1
]}

Shirai, Kiyoaki ^{[1
]}

机构：

[1] Japan Adv Inst Sci & Technol, Sch Informat Sci, 1-1 Asahidai, Nomi, Ishikawa 9231292, Japan

来源：

PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 | 2015年

关键词：

NOISE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07% in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43% and 6.07%. The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.

引用

页码：1354 / 1364

页数：11

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