Mining Sentiment for Web Short Texts Based on TSCM Model
HUANG Fa-liang1, LI Chao-xiong1, YUAN Chang-an2, WANG Yan1, YAO Zhi-qiang1
1. Faculty of Software, Fujian Normal University, Fuzhou, Fujian 350007, China; 2. School of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, Guangxi 530023, China
Abstract：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.
黄发良, 李超雄, 元昌安, 汪焱, 姚志强. 基于TSCM模型的网络短文本情感挖掘[J]. 电子学报, 2016, 44(8): 1887-1891.
HUANG Fa-liang, LI Chao-xiong, YUAN Chang-an, WANG Yan, YAO Zhi-qiang. Mining Sentiment for Web Short Texts Based on TSCM Model. Acta Electronica Sinica, 2016, 44(8): 1887-1891.
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2016, Vol. 44 
