Topic extraction from microblog posts using conversation structures


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.

Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: -
Event Title: 54th Annual Meeting of the Association for Computational Linguistics
Event Type: Other
Event Location: Humboldt University
Event Dates: 2016-08-07 - 2016-08-12
Uncontrolled Keywords: Language and Linguistics,Linguistics and Language
ISBN: 978-1-5108-2758-5
Full Text Link: http://www.aclw ... 16/P16-1199.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-08-15
Accepted Date: 2016-08-01
Authors: Li, Jing
Liao, Ming
Gao, Wei
He, Yulan ( 0000-0003-3948-5845)
Wong, Kam-Fai

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