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https://doi.org/10.21256/zhaw-3780| Publication type: | Conference paper |
| Type of review: | Not specified |
| Title: | Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams |
| Authors: | Jaggi, Martin Uzdilli, Fatih Cieliebak, Mark |
| DOI: | 10.21256/zhaw-3780 |
| Proceedings: | Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014) |
| Page(s): | 601 |
| Pages to: | 604 |
| Conference details: | International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014 |
| Issue Date: | 2014 |
| Publisher / Ed. Institution: | Association for Computational Linguistics |
| ISBN: | 978-1-63266-621-5 |
| Language: | English |
| Subjects: | Support vector machine; Classifier; Sentiment analysis |
| Subject (DDC): | 410.285: Computational linguistics |
| Abstract: | We describe a classifier to predict the message-level sentiment of English microblog messages from Twitter. This paper describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the system of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and additions of features, and additional sentiment lexicons. Furthermore, we used a sparse (l1-regularized) SVM, instead of the more commonly used l2-regularization, resulting in a very sparse linear classifier. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7366 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| SemEval105.pdf | 119.07 kB | Adobe PDF |  View/Open |
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Jaggi, M., Uzdilli, F., & Cieliebak, M. (2014). Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams [Conference paper]. Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 601–604. https://doi.org/10.21256/zhaw-3780
Jaggi, M., Uzdilli, F. and Cieliebak, M. (2014) ‘Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams’, in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Association for Computational Linguistics, pp. 601–604. Available at: https://doi.org/10.21256/zhaw-3780.
M. Jaggi, F. Uzdilli, and M. Cieliebak, “Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams,” in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 2014, pp. 601–604. doi: 10.21256/zhaw-3780.
JAGGI, Martin, Fatih UZDILLI und Mark CIELIEBAK, 2014. Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Conference paper. Association for Computational Linguistics. 2014. S. 601–604. ISBN 978-1-63266-621-5
Jaggi, Martin, Fatih Uzdilli, and Mark Cieliebak. 2014. “Swiss-Chocolate : Sentiment Detection Using Sparse SVMs and Part-of-Speech N-Grams.” Conference paper. In Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 601–4. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-3780.
Jaggi, Martin, et al. “Swiss-Chocolate : Sentiment Detection Using Sparse SVMs and Part-of-Speech N-Grams.” Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), Association for Computational Linguistics, 2014, pp. 601–4, https://doi.org/10.21256/zhaw-3780.
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