| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Keine Angabe |
| Titel: | Meta-classifiers easily improve commercial sentiment detection tools |
| Autor/-in: | Cieliebak, Mark Dürr, Oliver Uzdilli, Fatih |
| Tagungsband: | Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014) |
| Seite(n): | 3943 |
| Seiten bis: | 3947 |
| Angaben zur Konferenz: | 9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, 26-31 May 2014 |
| Erscheinungsdatum: | 2014 |
| Verlag / Hrsg. Institution: | Association for Computational Linguistics |
| ISBN: | 978-1-63266-621-5 |
| Sprache: | Englisch |
| Schlagwörter: | Opinion mining; Machine learning; Sentiment analysis; Corpus analytics |
| Fachgebiet (DDC): | 410.285: Computerlinguistik |
| Zusammenfassung: | In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7360 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Cieliebak, M., Dürr, O., & Uzdilli, F. (2014). Meta-classifiers easily improve commercial sentiment detection tools [Conference paper]. Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 3943–3947.
Cieliebak, M., Dürr, O. and Uzdilli, F. (2014) ‘Meta-classifiers easily improve commercial sentiment detection tools’, in Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Association for Computational Linguistics, pp. 3943–3947.
M. Cieliebak, O. Dürr, and F. Uzdilli, “Meta-classifiers easily improve commercial sentiment detection tools,” in Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 2014, pp. 3943–3947.
CIELIEBAK, Mark, Oliver DÜRR und Fatih UZDILLI, 2014. Meta-classifiers easily improve commercial sentiment detection tools. In: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Conference paper. Association for Computational Linguistics. 2014. S. 3943–3947. ISBN 978-1-63266-621-5
Cieliebak, Mark, Oliver Dürr, and Fatih Uzdilli. 2014. “Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.” Conference paper. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 3943–47. Association for Computational Linguistics.
Cieliebak, Mark, et al. “Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.” Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Association for Computational Linguistics, 2014, pp. 3943–47.
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