User group based emotion detection and topic discovery over short text

Jiachun FENG*, Yanghui RAO, Haoran XIE, Fu Lee WANG, Qing LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

20 Citations (Scopus)


In recent years, with the development of social media platforms, more and more people express their emotions online through short messages. It is quite valuable to detect emotions and relevant topics from such data. However, the feature sparsity of short texts brings challenges to joint topic-emotion models. In many cases, it is necessary to know not only what people think of specific topics, but also which individuals have similar feedback, and what characteristics of these users have. In this paper, we propose a user group based topic-emotion model named UGTE for emotions detection and topic discovery, which can alleviate the above feature sparsity problem of short texts. Specifically, the characteristics of each user are used to discover groups of individuals who share similar emotions, and UGTE aggregates short texts within a group into long pseudo-documents effectively. Experiments conducted on a real-world short text dataset validate the effectiveness of our proposed model.
Original languageEnglish
Pages (from-to)1553-1587
Number of pages35
JournalWorld Wide Web
Issue number3
Early online date12 Dec 2019
Publication statusPublished - May 2020

Bibliographical note

This work has been supported by Top-Up Fund (TFG-04) and Seed Fund (SFG-10) for General Research Fund / Early Career Scheme and Interdisciplinary Research Scheme of the Dean’s Research Fund 2018-19 (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19), Funding Support to General Research Fund Proposal (RG 39/2019-2020R) and the Internal Research Grant (RG 90/2018-2019R) of The Education University of Hong Kong, and LEO Dr David P. Chan Institute of Data Science, Lingnan University, Hong Kong. The work has also been supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Collaborative Research Fund, project number C1031-18G).


  • Joint topic-emotion model
  • Short text modeling
  • User characteristics
  • User group based mining


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