Mining subtopics and analyzing their sentiment dynamics on weblogs have many applications in multiple domains. Current work pays little attention to the combination of topics and their sentiment evolution simultaneously. In this paper, we study the problem of topic detection and sentiment-topic temporal evolution in weblogs, and propose a novel probabilistic model called topic sentiment trend model (TSTM). With the model, we can integrate the topic with sentiment, and analyze the temporal trend of the sentiment-topic. Experiments on two Chinese weblog datasets show that our approach is effective in modeling the topic facets and extracting their sentiment dynamics.
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