Universal affective model for readers’ emotion classification over short texts

Weiming LIANG, Haoran XIE*, Yanghui RAO, Raymond Y.K. LAU, Fu Lee WANG

*Corresponding author for this work

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

34 Citations (Scopus)


As the rapid development of Web 2.0 communities, social media service providers offer users a convenient way to share and create their own contents such as online comments, blogs, microblogs/tweets, etc. Understanding the latent emotions of such short texts from social media via the computational model is an important issue as such a model will help us to identify the social events and make better decisions (e.g., investment in stocking market). However, it is always very challenge to detect emotions from above user-generated contents due to the sparsity problem (e.g., a tweet is a short message). In this article, we propose an universal affective model (UAM) to classify readers’ emotions over unlabeled short texts. Different from conventional text classification model, the UAM structurally consists of topic-level and term-level sub-models, and detects social emotions from the perspective of readers in social media. Through the evaluation on real-world data sets, the experimental results validate the effectiveness of the proposed model in terms of the effectiveness and accuracy.

Original languageEnglish
Pages (from-to)322-333
Number of pages12
JournalExpert Systems with Applications
Early online date26 Jul 2018
Publication statusPublished - 30 Dec 2018
Externally publishedYes

Bibliographical note

The work described in this paper was fully supported by the Innovation and Technology Fund (Project No. GHP/022/17GD) from the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), Guangdong Science and Technology Program grant 2017A050506025, and the National Natural Science Foundation of China (61502545).


  • Biterm
  • Emotion classification
  • Short text
  • Topic model


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