Time-varying dynamic topic model: a better tool for mining microblogs at a global level

Han, Jun, Huang, Yu, Kumar, Kuldeep and Bhattacharya, Sukanto 2018, Time-varying dynamic topic model: a better tool for mining microblogs at a global level, Journal of global information management, vol. 26, no. 1, pp. 104-119, doi: 10.4018/JGIM.2018010106.

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Title Time-varying dynamic topic model: a better tool for mining microblogs at a global level
Author(s) Han, Jun
Huang, Yu
Kumar, Kuldeep
Bhattacharya, SukantoORCID iD for Bhattacharya, Sukanto orcid.org/0000-0001-6587-2879
Journal name Journal of global information management
Volume number 26
Issue number 1
Start page 104
End page 119
Total pages 16
Publisher IGI Global
Place of publication Hershey, Pa.
Publication date 2018-01
ISSN 1062-7375
Keyword(s) Dynamic Topic Models
Gibbs Sampler
Perplexity Measure
Text Mining
Summary In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of inherent dynamic features of microblogs thereby setting it apart from other online document mining methods in the extant literature. In summary, the authors' results show a better performance of mDTM in terms of the quality of the mined information compared to prior research and showcases mDTM as a promising tool for the effective mining of microblogs in a rapidly changing global information space.
Language eng
DOI 10.4018/JGIM.2018010106
Field of Research 0806 Information Systems
0807 Library And Information Studies
1702 Cognitive Science
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IGI Global
Persistent URL http://hdl.handle.net/10536/DRO/DU:30105142

Document type: Journal Article
Collection: Department of Management
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