Topic extraction from microblog posts using conversation structures

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Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the
effectiveness of our proposed model.

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Publication date15 Aug 2016
Publication titleThe 54th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Electronic)978-1-5108-2758-5
Original languageEnglish
Event54th Annual Meeting of the Association for Computational Linguistics - Humboldt University, Berlin, Germany
Duration: 7 Aug 201612 Aug 2016


Meeting54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016

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