Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IALP.2015.7451539 | 2015 International Conference on Asian Language Processing (IALP) |
Keywords | Field | DocType |
emotion analysis,code-switching,multi-view learning | Social media,Computer science,Code-switching,Emotion detection,Natural language processing,Artificial intelligence,Linguistics,Empirical research | Conference |
ISSN | ISBN | Citations |
2159-1962 | 978-1-4673-9595-3 | 0 |
PageRank | References | Authors |
0.34 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sophia Yat Mei Lee | 1 | 194 | 15.89 |
| Zhong-qing Wang | 2 | 140 | 20.28 |