Chinese Microblog Topic Detection through POS-Based Semantic Expansion

Abstract

A microblog is a new type of social media for information publishing, acquiring, and spreading. Finding the significant topics of a microblog is necessary for popularity tracing and public opinion following. This paper puts forward a method to detect topics from Chinese microblogs. Since traditional methods showed low performance on a short text from a microblog, we put forward a topic detection method based on the semantic description of the microblog post. The semantic expansion of the post supplies more information and clues for topic detection. First, semantic features are extracted from a microblog post. Second, the semantic features are expanded according to a thesaurus. Here TongYiCi CiLin is used as the lexical resource to find words with the same meaning. To overcome the polysemy problem, several semantic expansion strategies based on part-of-speech are introduced and compared. Third, an approach to detect topics based on semantic descriptions and an improved incremental clustering algorithm is introduced. A dataset from Sina Weibo is employed to evaluate our method. Experimental results show that our method can bring about better results both for post clustering and topic detection in Chinese microblogs. We also found that the semantic expansion of nouns is far more efficient than for other parts of speech. The potential mechanism of the phenomenon is also analyzed and discussed.