Topic extraction from microblog posts using conversation structures

Jing Li, Ming Liao, Wei Gao, Yulan He, Kam Fai Wong

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationSocial Media Content Analysis
Subtitle of host publicationNatural Language Processing and Beyond
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages419-437
Number of pages19
ISBN (Electronic)9789813223615
ISBN (Print)9789813223608
DOIs
Publication statusPublished - 1 Jan 2017

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Li, J., Liao, M., Gao, W., He, Y., & Wong, K. F. (2017). Topic extraction from microblog posts using conversation structures. In Social Media Content Analysis: Natural Language Processing and Beyond (pp. 419-437). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813223615_0026