Mostrar o rexistro simple do ítem
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification
dc.contributor.author | Vilares, David | |
dc.contributor.author | Doval, Yerai | |
dc.contributor.author | Alonso, Miguel A. | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-04-15T16:51:42Z | |
dc.date.available | 2024-04-15T16:51:42Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | David Vilares, Yerai Doval, Miguel A. Alonso, and Carlos Gómez-Rodríguez. 2016. LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 79–84, San Diego, California. Association for Computational Linguistics. | es_ES |
dc.identifier.isbn | 9781510826076 | |
dc.identifier.uri | http://hdl.handle.net/2183/36201 | |
dc.description.abstract | [Abstract]: In this paper we describe our deep learning approach for solving both two-, three- and fiveclass tweet polarity classification, and twoand five-class quantification. We first trained a convolutional neural network using pretrained Twitter word embeddings, so that we could extract the hidden activation values from the hidden layers once some input had been fed to the network. These values were then used as features for a support vector machine in both the classification and quantification subtasks, together with additional linguistic information in the former scenario. The results obtained for the classification subtasks show that this approach performs better than a single convolutional network, and for the quantification part it also yields good results. Official rankings locate us: 2nd (practically tied with 1st) for the binary classification task, 2nd for binary quantification and 4th (practically tied with 3rd) for the five-class polarity classification challenge. | es_ES |
dc.description.sponsorship | This research is supported by the Ministerio de Economía y Competitividad (FFI2014-51978-C2). David Vilares is funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180). Yerai Doval is funded by the Ministerio de Economía y Competitividad (BES-2015-073768). Carlos Gómez-Rodríguez is funded by an Oportunius program grant (Xunta de Galicia) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FFI2014-51978-C2-1-R/ES/TECNOLOGIAS DE LA LENGUA PARA ANALISIS DE OPINIONES EN REDES SOCIALES | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU13%2F01180/ES/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BES-2015-073768/ES/ | es_ES |
dc.relation.uri | https://doi.org/10.18653/v1/S16-1009 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | es_ES | |
dc.subject | Sentiment Analysis | es_ES |
dc.subject | Convolutional neural network | es_ES |
dc.title | LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.volume | 2016 | es_ES |
UDC.startPage | 79 | es_ES |
UDC.endPage | 84 | es_ES |
dc.identifier.doi | 10.18653/v1/S16-1009 | |
UDC.conferenceTitle | Semantic Evaluation. International Workshop. 10th 2016. (SemEval 2016) | es_ES |