Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-3779
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dürr, Oliver | - |
dc.contributor.author | Uzdilli, Fatih | - |
dc.contributor.author | Cieliebak, Mark | - |
dc.date.accessioned | 2018-06-26T14:41:44Z | - |
dc.date.available | 2018-06-26T14:41:44Z | - |
dc.date.issued | 2014 | - |
dc.identifier.isbn | 978-1-941643-24-2 | de_CH |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/7363 | - |
dc.description.abstract | In this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 general purpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points. | de_CH |
dc.language.iso | en | de_CH |
dc.publisher | Association for Computational Linguistics | de_CH |
dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
dc.subject | Sentiment analysis | de_CH |
dc.subject | Random forest | de_CH |
dc.subject | Competition | de_CH |
dc.subject | Ensemble method | de_CH |
dc.subject.ddc | 410.285: Computerlinguistik | de_CH |
dc.title | JOINT_FORCES : unite competing sentiment classifiers with random forest | de_CH |
dc.type | Konferenz: Paper | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
dc.identifier.doi | 10.21256/zhaw-3779 | - |
zhaw.conference.details | International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.pages.end | 369 | de_CH |
zhaw.pages.start | 366 | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.publication.review | Not specified | de_CH |
zhaw.title.proceedings | Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014) | de_CH |
zhaw.webfeed | Software Systems | de_CH |
zhaw.webfeed | Natural Language Processing | de_CH |
Appears in collections: | Publikationen School of Engineering |
Files in This Item:
File | Description | Size | Format | |
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SemEval062-1.pdf | 428.43 kB | Adobe PDF | View/Open |
Show simple item record
Dürr, O., Uzdilli, F., & Cieliebak, M. (2014). JOINT_FORCES : unite competing sentiment classifiers with random forest [Conference paper]. Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 366–369. https://doi.org/10.21256/zhaw-3779
Dürr, O., Uzdilli, F. and Cieliebak, M. (2014) ‘JOINT_FORCES : unite competing sentiment classifiers with random forest’, in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Association for Computational Linguistics, pp. 366–369. Available at: https://doi.org/10.21256/zhaw-3779.
O. Dürr, F. Uzdilli, and M. Cieliebak, “JOINT_FORCES : unite competing sentiment classifiers with random forest,” in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 2014, pp. 366–369. doi: 10.21256/zhaw-3779.
DÜRR, Oliver, Fatih UZDILLI und Mark CIELIEBAK, 2014. JOINT_FORCES : unite competing sentiment classifiers with random forest. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Conference paper. Association for Computational Linguistics. 2014. S. 366–369. ISBN 978-1-941643-24-2
Dürr, Oliver, Fatih Uzdilli, and Mark Cieliebak. 2014. “JOINT_FORCES : Unite Competing Sentiment Classifiers with Random Forest.” Conference paper. In Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 366–69. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-3779.
Dürr, Oliver, et al. “JOINT_FORCES : Unite Competing Sentiment Classifiers with Random Forest.” Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), Association for Computational Linguistics, 2014, pp. 366–69, https://doi.org/10.21256/zhaw-3779.
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