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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1525
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dc.contributor.authorDeriu, Jan Milan-
dc.contributor.authorLucchi, Aurelien-
dc.contributor.authorDe Luca, Valeria-
dc.contributor.authorSeveryn, Aliaksei-
dc.contributor.authorMüller, Simone-
dc.contributor.authorCieliebak, Mark-
dc.contributor.authorHofmann, Thomas-
dc.contributor.authorJaggi, Martin-
dc.date.accessioned2017-12-14T14:14:52Z-
dc.date.available2017-12-14T14:14:52Z-
dc.date.issued2017-
dc.identifier.isbn9781450349130de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1851-
dc.description.abstractThis paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse – but still acceptable – performance when compared to the single language model, while benefiting from better generalization properties across languages.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSentiment Analysisde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleLeveraging large amounts of weakly supervised data for multi-language sentiment classificationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1145/3038912.3052611de_CH
dc.identifier.doi10.21256/zhaw-1525-
zhaw.conference.details26th International World Wide Web Conference Committee (IW3C2), Perth, Australia, 3-7 April 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1052de_CH
zhaw.pages.start1045de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the 26th International Conference on World Wide Webde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.funding.zhawDeepText: Intelligente Textanalyse mit Deep Learningde_CH
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Deriu, J. M., Lucchi, A., De Luca, V., Severyn, A., Müller, S., Cieliebak, M., Hofmann, T., & Jaggi, M. (2017). Leveraging large amounts of weakly supervised data for multi-language sentiment classification [Conference paper]. Proceedings of the 26th International Conference on World Wide Web, 1045–1052. https://doi.org/10.1145/3038912.3052611
Deriu, J.M. et al. (2017) ‘Leveraging large amounts of weakly supervised data for multi-language sentiment classification’, in Proceedings of the 26th International Conference on World Wide Web. Association for Computing Machinery, pp. 1045–1052. Available at: https://doi.org/10.1145/3038912.3052611.
J. M. Deriu et al., “Leveraging large amounts of weakly supervised data for multi-language sentiment classification,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1045–1052. doi: 10.1145/3038912.3052611.
DERIU, Jan Milan, Aurelien LUCCHI, Valeria DE LUCA, Aliaksei SEVERYN, Simone MÜLLER, Mark CIELIEBAK, Thomas HOFMANN und Martin JAGGI, 2017. Leveraging large amounts of weakly supervised data for multi-language sentiment classification. In: Proceedings of the 26th International Conference on World Wide Web. Conference paper. Association for Computing Machinery. 2017. S. 1045–1052. ISBN 9781450349130
Deriu, Jan Milan, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simone Müller, Mark Cieliebak, Thomas Hofmann, and Martin Jaggi. 2017. “Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification.” Conference paper. In Proceedings of the 26th International Conference on World Wide Web, 1045–52. Association for Computing Machinery. https://doi.org/10.1145/3038912.3052611.
Deriu, Jan Milan, et al. “Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification.” Proceedings of the 26th International Conference on World Wide Web, Association for Computing Machinery, 2017, pp. 1045–52, https://doi.org/10.1145/3038912.3052611.


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