Abstract
It is a critical task to infer discriminative and coherent topics from short texts. Furthermore, people not only want to know what kinds of topics can be extract from these short texts, but also desire to obtain the temporal dynamic evolution of these topics. In this paper, we present a novel model for short texts, referred as topic trend detection (TTD) model. Based on an optimized topic model we proposed, TTD model derives more typical terms and itemsets to represent topics of short texts and improves the coherence of topic representations. Ultimately, we extend the topic itemsets obtained from the optimized topic model by word embeddings to detect topic trends. Through extensive experiments on several real-world short text collections in Sina Microblog, the result demonstrate our method achieves comparable topic representations than state-of-the-art models, measured by topic coherence, and then show its application in identifying topic trends in Sina Microblog.
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