Orthographic features for emotion classification in Chinese in informal short texts.
Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by prop...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 2; pp. 329 - 353 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Jun2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=150471575&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 150471575 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2021 vid: 55 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 150471575 10.1007/s10579-020-09515-3 ppf: 329 ppct: 24 formats: fmt: @attributes: type: P size: 364KB tig: atl: Orthographic features for emotion classification in Chinese in informal short texts. aug: au: Chen, I-Hsuan Long, Yunfei Lu, Qin Huang, Chu-Ren affil: Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hung Hom, China Department of Computing, Hong Kong Polytechnic University, Hung Hom, China School of Computer Science and Electronic Engineering, University of Essex, Hung Hom, China su: Deep learning Emotions Machine learning Classification sug: subj: Deep learning Emotions Machine learning Classification keyword: Code-switching Emotion classification Morpho-syntactic features Orthographic code mixing Orthographic features Orthography Short text ab: Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by proposing orthographic features based on orthographic code mixing and code-switching for both non-ML and ML approaches. Our results show that orthographic features routinely outperform grammatical features for emotion classification for short texts in all approaches as expected. Orthographic features were also shown to make more significant contributions, especially in terms of precision and in formal texts when state of the art deep learning algorithms are applied. This result confirms the effectiveness of the orthographic change feature to the task of emotion classification. These results are argued to be applicable to all languages because of the common code-shifting in languages with non-Latin orthographies, and the use of non-letter symbols in all languages. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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