Fast Opinion Mining using Information Retrieval techniques

Detalhes bibliográficos
Autor(a) principal: Ortiz Bascuas, Jose Antonio
Data de Publicação: 2020
Tipo de documento: Artigo
Idioma: eng
Título da fonte: INFOCOMP: Jornal de Ciência da Computação
Texto Completo: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025
Resumo: This paper focuses on the construction of models, through automatic learning, for sentimental analysis, which allow to obtain the polarity of a tweet by taking advantage of the information obtained through an information retrieval process. For this purpose, the features derived from the classification generated by such a system in response to the consultation of the document to be analyzed will be used. Through this combination of tools we will achieve a language-independent sentiment analysis, reaching accuracies comparable to other state-of-the art approaches but at a much higher speed.  
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spelling Fast Opinion Mining using Information Retrieval techniquesThis paper focuses on the construction of models, through automatic learning, for sentimental analysis, which allow to obtain the polarity of a tweet by taking advantage of the information obtained through an information retrieval process. For this purpose, the features derived from the classification generated by such a system in response to the consultation of the document to be analyzed will be used. Through this combination of tools we will achieve a language-independent sentiment analysis, reaching accuracies comparable to other state-of-the art approaches but at a much higher speed.  Editora da UFLA2020-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025INFOCOMP Journal of Computer Science; Vol. 19 No. 2 (2020): December 2020; 120-1311982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025/548Copyright (c) 2020 Jose Antonio Ortiz Bascuasinfo:eu-repo/semantics/openAccessOrtiz Bascuas, Jose Antonio2020-12-01T21:34:08Zoai:infocomp.dcc.ufla.br:article/1025Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:45.956100INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Fast Opinion Mining using Information Retrieval techniques
title Fast Opinion Mining using Information Retrieval techniques
spellingShingle Fast Opinion Mining using Information Retrieval techniques
Ortiz Bascuas, Jose Antonio
title_short Fast Opinion Mining using Information Retrieval techniques
title_full Fast Opinion Mining using Information Retrieval techniques
title_fullStr Fast Opinion Mining using Information Retrieval techniques
title_full_unstemmed Fast Opinion Mining using Information Retrieval techniques
title_sort Fast Opinion Mining using Information Retrieval techniques
author Ortiz Bascuas, Jose Antonio
author_facet Ortiz Bascuas, Jose Antonio
author_role author
dc.contributor.author.fl_str_mv Ortiz Bascuas, Jose Antonio
description This paper focuses on the construction of models, through automatic learning, for sentimental analysis, which allow to obtain the polarity of a tweet by taking advantage of the information obtained through an information retrieval process. For this purpose, the features derived from the classification generated by such a system in response to the consultation of the document to be analyzed will be used. Through this combination of tools we will achieve a language-independent sentiment analysis, reaching accuracies comparable to other state-of-the art approaches but at a much higher speed.  
publishDate 2020
dc.date.none.fl_str_mv 2020-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025
url https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1025/548
dc.rights.driver.fl_str_mv Copyright (c) 2020 Jose Antonio Ortiz Bascuas
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 Jose Antonio Ortiz Bascuas
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editora da UFLA
publisher.none.fl_str_mv Editora da UFLA
dc.source.none.fl_str_mv INFOCOMP Journal of Computer Science; Vol. 19 No. 2 (2020): December 2020; 120-131
1982-3363
1807-4545
reponame:INFOCOMP: Jornal de Ciência da Computação
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str INFOCOMP: Jornal de Ciência da Computação
collection INFOCOMP: Jornal de Ciência da Computação
repository.name.fl_str_mv INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv infocomp@dcc.ufla.br||apfreire@dcc.ufla.br
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