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User group based emotion detection and topic discovery over short text

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Abstract

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.

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Acknowledgment

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).

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Authors and Affiliations

  1. School of Data and Computer science, Sun Yat-sen University, Guangdong, China

    Jiachun Feng & Yanghui Rao

  2. Department of Computing and Decision Sciences, Lingnan University, New Territories, Hong Kong

    Haoran Xie

  3. School of Science and Technology, The Open University of Hong Kong, Kowloon, Hong Kong

    Fu Lee Wang

  4. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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  1. Jiachun Feng
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  2. Yanghui Rao
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  4. Fu Lee Wang
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  5. Qing Li
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Correspondence to Yanghui Rao.

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Appendix:

Appendix:

For clarity, numerical results of Figures 2–7 are provided as follows.

Table 9 Coherence@10 of UGTE_ID and baselines with different topic numbers when |G| = 10, where the best results are highlighted in boldface
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Table 10 Coherence@20 of UGTE_ID and baselines with different topic numbers when |G| = 10, where the best results are highlighted in boldface
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Table 11 Coherence@30 of UGTE_ID and baselines with different topic numbers when |G| = 10, where the best results are highlighted in boldface
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Table 12 Accuracy of UGTE_ID and baselines with different topic numbers when |G| = 10, where the best results are highlighted in boldface
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Table 13 Kappa Score of UGTE_ID and baselines with different topic numbers when |G| = 10, where the best results are highlighted in boldface
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Table 14 The mean and variance of topic discovery and emotion discovery of UGTE_ID over different numbers of user groups, where the best results are highlighted in boldface
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Table 15 The mean and variance values of impact of extremely short text on UGTE_ID and MSTM
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Feng, J., Rao, Y., Xie, H. et al. User group based emotion detection and topic discovery over short text. World Wide Web 23, 1553–1587 (2020). https://doi.org/10.1007/s11280-019-00760-3

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