谢 珺;郝 洁;苏婧琼;邹雪君;李思宇. 一种针对短文本的主题情感混合模型[J]. 中文信息学报, 2017, 31(1): 162-168.
XIE Jun; HAO Jie; SU Jingqiong; ZOU Xuejun; LI Siyu. A Joint Topic and Sentiment Model for Short Texts. , 2017, 31(1): 162-168.
一种针对短文本的主题情感混合模型
谢 珺,郝 洁,苏婧琼,邹雪君,李思宇
太原理工大学 信息工程学院,山西 晋中 030600
A Joint Topic and Sentiment Model for Short Texts
XIE Jun, HAO Jie, SU Jingqiong, ZOU Xuejun, LI Siyu
Information Engineering College, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
Abstract：The joint topic and sentiment model is aimed at efficiently detecting topics and emotions for the given corpus. Faced with the sparsity of short texts and the lack of sentiment/topic analysis methods, this paper proposes a novel way called Biterm Joint Sentiment Topic Model (BJSTM). A sentiment layer is added to Biterm Topic Model, thus a three-layer Bayesian model of “sentiment-topic-term” is formed. By sampling the sentiment and topic of each biterm, BJSTM could depict the word co-occurrence of the whole corpus and overcome the sparsity of short texts to some extent. The experimental results show that BJSTM gets better performance in sentiment classification as well as topic extraction.
2017, Vol. 31 
