Fast Supervised Topic Models for Short Text Emotion Detection

Jianhui Pang, Yanghui Rao, Haoran Xie, Xizhao Wang, Fu Lee Wang, Tak Lam Wong, Qing Li

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

With the development of social network platforms, discussion forums, and question answering websites, a huge number of short messages that typically contain a few words for an individual document are posted by online users. In these short messages, emotions are frequently embedded for communicating opinions, expressing friendship, and promoting influence. It is quite valuable to detect emotions from short messages, but the corresponding task suffers from the sparsity of feature space. In this article, we first generate term groups co-occurring in the same context to enrich the number of features. Then, two basic supervised topic models are proposed to associate emotions with topics accurately. To reduce the time cost of parameter estimation, we further propose an accelerated algorithm for our basic models. Extensive evaluations using three short corpora validate the efficiency and effectiveness of the accelerated models for predicting the emotions of unlabeled documents, in addition to generate the topic-level emotion lexicons.

Original languageEnglish
Article number8852720
Pages (from-to)815-828
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume51
Issue number2
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Accelerated algorithm
  • emotion detection
  • short text analysis
  • topic model

Fingerprint

Dive into the research topics of 'Fast Supervised Topic Models for Short Text Emotion Detection'. Together they form a unique fingerprint.

Cite this