Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70899
Title: Emotion analysis in code-switching text with joint factor graph model
Authors: Wang, ZQ 
Lee, SYM 
Li, SS
Zhou, GD
Issue Date: 2017
Source: IEEE/ACM transactions on audio, speech, and language processing, 2017, v. 25, no. 3, p. 469-480
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.
Keywords: Bilingual information
Code-switching
Emotion analysis
Factor graph model
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE/ACM transactions on audio, speech, and language processing 
ISSN: 2329-9290
DOI: 10.1109/TASLP.2016.2637280
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