Fast Opinion Mining using Information Retrieval techniques
Autor(a) principal: | |
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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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INFOCOMP: Jornal de Ciência da Computação |
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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 |
_version_ |
1799874742643064832 |