TY - GEN
T1 - Topic extraction from microblog posts using conversation structures
AU - Li, Jing
AU - Liao, Ming
AU - Gao, Wei
AU - He, Yulan
AU - Wong, Kam-Fai
N1 - -
PY - 2016/8/15
Y1 - 2016/8/15
N2 - 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 theeffectiveness of our proposed model.
AB - 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 theeffectiveness of our proposed model.
UR - http://www.scopus.com/inward/record.url?scp=85011977266&partnerID=8YFLogxK
M3 - Conference publication
AN - SCOPUS:85011977266
VL - 4
SP - 2114
EP - 2123
BT - The 54th Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics
T2 - 54th Annual Meeting of the Association for Computational Linguistics
Y2 - 7 August 2016 through 12 August 2016
ER -