A comparison of domain-based word polarity estimation using different word embeddings

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• dc.contributor.author García-Pablos, Aitorca
• dc.contributor.author Cuadros Oller, Montseca
• dc.contributor.author Rigau Claramunt, Germanca
• dc.date.accessioned 2018-01-22T10:30:49Z
• dc.date.available 2018-01-22T10:30:49Z
• dc.date.issued 2016
• dc.description Comunicació presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovènia.
• dc.description.abstract A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus.en
• dc.description.sponsorship This work has been supported by Vicomtech-IK4 and partially funded by TUNER project (TIN2015-65308-C5-1-R).
• dc.format.mimetype application/pdfca
• dc.identifier.citation García-Pablos A, Cuadros M, Rigau G. A comparison of domain-based word polarity estimation using different word embeddings. In: Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S, editors. LREC 2016. Tenth International Conference on Language Resources and Evaluation; 2016 May 23-28; Portorož, Slovenia. [Paris]: ELRA; 2016. p. 54-60.
• dc.identifier.uri http://hdl.handle.net/10230/33709
• dc.language.iso eng
• dc.publisher ELRA (European Language Resources Association)ca
• dc.relation.ispartof Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S, editors. LREC 2016. Tenth International Conference on Language Resources and Evaluation; 2016 May 23-28; Portorož, Slovenia. [Paris]: ELRA; 2016. p. 54-60.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-1-R
• dc.rights © 2016 ELRA - European Language Resources Association. All rights reserved. The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
• dc.subject.keyword Sentiment lexiconen
• dc.subject.keyword Sentiment analysisen
• dc.subject.keyword Word embeddingen
• dc.title A comparison of domain-based word polarity estimation using different word embeddingsca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion