期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2013
卷号：2
期号：7
页码：2189-2194
出版社：IJECS
摘要：Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST).JST model based on latent Dirichlet allocation (LDA), supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains. The weakly-supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on datasets from five different domains. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web
