Topic-Sentiment Mining from Multiple Text Collections

摘要：

　　Topic-sentiment mining is a challenging task for many applications.This paper presents a topic-sentiment joint model in order to mine topics and their sentimental polarities from multiple text collections.Text collections are represented with a mixture of components and modeled via the hierarchical Dirichlet process which can determine the number of components automatically.Each component consists of topic words and its sentiments.The model can mine topics with different proportions and different sentimental polarities as well as one positive and one negative topic for each collection.Experiments on two text collections from Chinese news media and microblog show that our model can find meaningful topics and their different sentimental polarities.Experiments on Multi-Domain Sentiment Dataset show that our model is better than the JST-alike models on parameter settings for topic-sentiment mining.

关键词： text mining topic modeling sentiment analysis hierarchical Dirichlet process

作者: Qifeng Zhu Fang Li

作者单位: Dept.of Computer Science and Engineering Shanghai Jiao Tong University Shanghai,China 200240

会议类型: 国内会议

会议名称: 第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

会议地点: 烟台

会议语种:英文

页码: 1-12

在线出版日期: 2016-10-14（万方平台首次上网日期，不代表论文的发表时间）