Mining sentiment for web short texts based on TSCM model

被引：0

作者：

Huang F.-L. ^{[1
]}

Li C.-X. ^{[1
]}

Yuan C.-A. ^{[2
]}

Wang Y. ^{[1
]}

Yao Z.-Q. ^{[1
]}

机构：

[1] Faculty of Software, Fujian Normal University, Fuzhou, 350007, Fujian

[2] School of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, 530023, Guangxi

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2016年 / 44卷 / 08期

关键词：

Latent dirichlet allocation (LDA); Sentiment analysis; Topic sentiment mixture;

D O I：

10.3969/j.issn.0372-2112.2016.08.017

中图分类号：

学科分类号：

摘要：

For sentiment analysis of web short texts, a topic sentiment combining model (TSCM) is proposed based on LDA and web review behavioral theory, which is founded on the assumption that topic distribution of each sentence in a review is unique and different from that of other sentences. Generative process of TSCM is to first determine sentiment orientation of each word and then topic of each sentence in a review while taking word relation into consideration. Extensive experiments on real-world datasets (Movie and Amazon) show that TSCM significantly outperforms JST, S-LDA, D-PLDA and SAS in terms of the accuracy of sentiment classification and topic detection. © 2016, Chinese Institute of Electronics. All right reserved.

引用

页码：1887 / 1891

页数：4

共 16 条

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