document: Topic sentiment mixture : modeling facets and opinions in weblogs In this paper , we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously . The proposed Topic-Sentiment Mixture ( TSM ) model can reveal the latent topical facets in a Weblog collection , the subtopics in the results of an ad hoc query , and their associated sentiments . It could also provide general sentiment models that are applicable to any ad hoc topics . With a specifically designed HMM structure , the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics . Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections . The TSM model is quite general ; it can be applied to any text collections with a mixture of topics and sentiments , thus has many potential applications , such as search result summarization , opinion tracking , and user behavior prediction .
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