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.
|Title of host publication||Social Media Content Analysis|
|Subtitle of host publication||Natural Language Processing and Beyond|
|Publisher||World Scientific Publishing Co. Pte Ltd|
|Number of pages||19|
|Publication status||Published - 1 Jan 2017|
ASJC Scopus subject areas
- Computer Science(all)