Emotion Analysis in Code-Switching Text with Joint Factor Graph Model

Zhongqing Wang, Yat Mei Lee, Shoushan Li, Guodong Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

Previous research on emotions analysis has placed much emphasis in monolingual instead of bilingual text. However, emotions on social media platforms are often found in bilingual or code-switching posts. Different from monolingual text, emotions in code-switching text can be expressed in both monolingual and bilingual forms. Moreover, more than one emotion can be expressed within a single post; yet they tend to be related in some ways which offers some implications. It is thus necessary to consider the correlation between different emotions. In this paper, a joint factor graph model is proposed to address this issue. In particular, attribute functions of the factor graph model are utilized to learn both monolingual and bilingual information from each post, factor functions are used to explore the relationship among different emotions, and a belief propagation algorithm is employed to learn and predict the model. Empirical studies demonstrate the importance of emotion analysis in code-switching text and the effectiveness of our proposed joint learning model.
Original languageEnglish
Pages (from-to)469-480
Number of pages12
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume25
Issue number3
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • Bilingual information
  • code-switching
  • emotion analysis
  • factor graph model

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Instrumentation
  • Acoustics and Ultrasonics
  • Linguistics and Language
  • Electrical and Electronic Engineering
  • Speech and Hearing

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