Topic-SentimentMixture：ModelingFacetsand..ppt

Topic-Sentiment Mixture: Modeling Facets and Opinions in Weblogs Qiaozhu Mei?, Xu Ling?, Matthew Wondra?, Hang Su?, and ChengXiang Zhai? Why Opinion Analysis? Customers: need peer opinions to make purchase decisions Business providers: need customers’ opinions to improve product need to track opinions to make marketing decisions Social researchers: want to know people’s reactions about social events Government: wants to know people’s reactions to a new policy Psychology, education, etc. An Illustrative Example Why Extracting Opinions from Blogs? Easy to collect: huge amount, clean format Broadly distributed: demographics Topic diversified: free discussion about any topic/product/event Opinion rich: highly personalized Evidence from Blog Search Existing Blog-opinion Analysis Work Opinmind: sentiment classification/search of blogs Existing Blog-opinion Analysis Work (Cont.) What’s Missing Here? Discussions are faceted E.g. iPod: battery? Price? Nano? … Usually different opinions on different facets Opinions have polarities Positive, negative, and neutral … Non-discriminative analysis may lead to wrong decision Opinions are changing over time … Our Goal Model the mixture of facets and opinions (topics and sentiments) Generate a faceted opinion summarization for ad hoc query Track the change of opinions over time Challenges in Opinion Analysis from Blogs Topics and sentiments are mixed together No existing facet structure for ad hoc topics Difficult to identify sentiment polarities Difficult to associate sentiment polarities with facets Difficult to segment topics and sentiments Tracking sentiment dynamics Our Approach: Modeling Topic-Sentiment Mixture Use language models to represent facets and sentiments Facets represented with topic models, extracted in an unsupervised/semi-supervised way Sentiment models extracted in a supervised way Model the mixture of topics and sentiments with a probabilistic generative model Segment associated topics and sentiments with