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https://doi.org/10.21256/zhaw-3780| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Keine Angabe |
| Titel: | Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams |
| Autor/-in: | Jaggi, Martin Uzdilli, Fatih Cieliebak, Mark |
| DOI: | 10.21256/zhaw-3780 |
| Tagungsband: | Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014) |
| Seite(n): | 601 |
| Seiten bis: | 604 |
| Angaben zur Konferenz: | International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014 |
| Erscheinungsdatum: | 2014 |
| Verlag / Hrsg. Institution: | Association for Computational Linguistics |
| ISBN: | 978-1-63266-621-5 |
| Sprache: | Englisch |
| Schlagwörter: | Support vector machine; Classifier; Sentiment analysis |
| Fachgebiet (DDC): | 410.285: Computerlinguistik |
| Zusammenfassung: | 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 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| SemEval105.pdf | 119.07 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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