Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2016

Abstract

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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)

First Page

2114

Last Page

2123

Identifier

10.18653/v1/P16-1199

Publisher

Association for Computational Linguistics

City or Country

Berlin, Germany

Additional URL

https://doi.org/10.18653/v1/P16-1199

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