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http://hdl.handle.net/10397/76642
| Title: | Multi-view learning for emotion detection in code-switching texts | Authors: | Lee, SYM Wang, Z |
Issue Date: | 2016 | Source: | International Conference on Asian Language Processing, IALP 2015, Suzhou, China, 24 - 25 October 2015, 7451539, p. 90-93 | Abstract: | Previous researches have placed emphasis on analyzing emotions in monolingual text, neglecting the fact that emotions are often found in bilingual or code-switching posts in social media. Traditional methods for the identification or classification of emotion fail to accommodate the code-switching content. To address this challenge, in this paper, we propose a multi-view learning framework to learn and detect the emotions through both monolingual and bilingual views. In particular, the monolingual views are extracted from the monolingual text separately, and the bilingual view is constructed with both monolingual and translated text collectively. Empirical studies demonstrate the effectiveness of our proposed approach in detecting emotions in code-switching texts. | Keywords: | Code-switching Emotion analysis Multi-view learning |
Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 9781467395953 | DOI: | 10.1109/IALP.2015.7451539 | Rights: | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication S. Y. M. Lee and Zhongqing Wang, "Multi-view learning for emotion detection in code-switching texts," 2015 International Conference on Asian Language Processing (IALP), 2015, pp. 90-93 is available at https://dx.doi.org/10.1109/IALP.2015.7451539. |
| Appears in Collections: | Conference Paper |
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| Lee_Emotion_Detection_Code-switching.pdf | Pre-Published version | 923.26 kB | Adobe PDF | View/Open |
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