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On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages
dc.contributor.author | Vilares, David | |
dc.contributor.author | Alonso, Miguel A. | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-01-17T19:53:40Z | |
dc.date.available | 2024-01-17T19:53:40Z | |
dc.date.issued | 2015-09 | |
dc.identifier.citation | Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2015), On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages. J Assn Inf Sci Tec, 66: 1799-1816. https://doi.org/10.1002/asi.23284 | es_ES |
dc.identifier.issn | 2330-1643 | |
dc.identifier.issn | 2330-1635 | |
dc.identifier.uri | http://hdl.handle.net/2183/34970 | |
dc.description | This is the peer reviewed version of the following article: Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2015), ‘On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages’. J Assn Inf Sci Tec, 66: 1799-1816, which has been published in final form at https://doi.org/10.1002/asi.23284. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | es_ES |
dc.description.abstract | [Abstract]: Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product, or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focused on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic, and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used. | es_ES |
dc.description.sponsorship | Research reported in this article has been partially funded by the Ministerio de Economía y Competitividad and FEDER (Grant TIN2010–18552-C03-02) and by the Xunta de Galicia (Grants CN2012/008, CN2012/319). | es_ES |
dc.description.sponsorship | Xunta de Galicia; CN2012/008 | es_ES |
dc.description.sponsorship | Xunta de Galicia; CN2012/319 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/TIN2010-18552-C03-02/ES/ANALISIS DE TEXTOS Y RECUPERACION DE INFORMACION PARA LA MINERIA DE OPINIONES: ANALISIS DE ENUNCIADOS Y EXTRACCION DE RELACIONES | es_ES |
dc.relation.isversionof | https://doi.org/10.1002/asi.23284 | |
dc.relation.uri | https://doi.org/10.1002/asi.23284 | es_ES |
dc.rights | Todos os dereitos reservados. All rights reserved. | es_ES |
dc.subject | Sentiment Analysis | es_ES |
dc.subject | Natural language processing | es_ES |
dc.subject | Opinion mining | es_ES |
dc.subject | Multilingual Parsing | es_ES |
dc.title | On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Journal of the Association for Information Science and Technology | es_ES |
UDC.volume | 66 | es_ES |
UDC.issue | 9 | es_ES |
UDC.startPage | 1799 | es_ES |
UDC.endPage | 1816 | es_ES |
dc.identifier.doi | 10.1002/asi.23284 |
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