Mining the personal interests of microbloggers via exploiting wikipedia knowledge
|Mining the personal interests of microbloggers via exploiting wikipedia knowledge|
|Author(s)||Fan M., Zhou Q., Zheng T.F.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||interest, Microblog, social tagging, Wikipedia (Extra: Computational linguistics, Semantics, Text processing, Evaluation function, interest, Micro-blog, Micro-blogging platforms, Social tagging, State-of-the-art methods, Textual information, Wikipedia, Research)|
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Mining the personal interests of microbloggers via exploiting wikipedia knowledge is a 2014 conference paper written in English by Fan M., Zhou Q., Zheng T.F. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
This paper focuses on an emerging research topic about mining microbloggers' personalized interest tags from their own microblogs ever posted. It based on an intuition that microblogs indicate the daily interests and concerns of microblogs. Previous studies regarded the microblogs posted by one microblogger as a whole document and adopted traditional keyword extraction approaches to select high weighting nouns without considering the characteristics of microblogs. Given the less textual information of microblogs and the implicit interest expression of microbloggers, we suggest a new research framework on mining microbloggers' interests via exploiting the Wikipedia, a huge online word knowledge encyclopedia, to take up those challenges. Based on the semantic graph constructed via the Wikipedia, the proposed semantic spreading model (SSM) can discover and leverage the semantically related interest tags which do not occur in one's microblogs. According to SSM, An interest mining system have implemented and deployed on the biggest microblogging platform (Sina Weibo) in China. We have also specified a suite of new evaluation metrics to make up the shortage of evaluation functions in this research topic. Experiments conducted on a real-time dataset demonstrate that our approach outperforms the state-of-the-art methods to identify microbloggers' interests.
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