A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis

Sentiment analysis is a text mining task that determines the polarity of a given text, i.e., its positiveness or negativeness. Recently, it has received a lot of attention given the interest in opinion mining in micro-blogging platforms. These new forms of textual expressions present new challenges...

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Detalles Bibliográficos
Autores: Oscar Sánchez Siordia, Eric Tellez, SABINO MIRANDA JIMENEZ, Mario Graff, Daniela Moctezuma, Elio Atenógenes Villaseñor García
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:México
Institución:Centro de Investigación en Ciencias de Información Geoespacial
Repositorio:Repositorio Institucional Centro GEO
Idioma:inglés
OAI Identifier:oai:centrogeo.repositorioinstitucional.mx:1012/243
Acceso en línea:http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243
Access Level:acceso embargado
Palabra clave:info:eu-repo/classification/Autor/Sentiment Analysis
info:eu-repo/classification/Autor/Error-robust text representations
info:eu-repo/classification/Autor/Opinion mining
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/33
info:eu-repo/classification/cti/3304
info:eu-repo/classification/cti/120304
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spelling A Case Study of Spanish Text Transformations for Twitter Sentiment AnalysisOscar Sánchez SiordiaEric TellezSABINO MIRANDA JIMENEZMario GraffDaniela MoctezumaElio Atenógenes Villaseñor Garcíainfo:eu-repo/classification/Autor/Sentiment Analysisinfo:eu-repo/classification/Autor/Error-robust text representationsinfo:eu-repo/classification/Autor/Opinion mininginfo:eu-repo/classification/cti/7info:eu-repo/classification/cti/33info:eu-repo/classification/cti/3304info:eu-repo/classification/cti/120304info:eu-repo/classification/cti/120304Sentiment analysis is a text mining task that determines the polarity of a given text, i.e., its positiveness or negativeness. Recently, it has received a lot of attention given the interest in opinion mining in micro-blogging platforms. These new forms of textual expressions present new challenges to analyze text because of the use of slang, orthographic and grammatical errors, among others. Along with these challenges, a practical sentiment classifier should be able to handle efficiently large workloads. The aim of this research is to identify in a large set of combinations which text transformations (lemmatization, stemming, entity removal, among others), tokenizers (e.g., word n-grams), and token-weighting schemes make the most impact on the accuracy of a classifier (Support Vector Machine) trained on two Spanish datasets. The methodology used is to exhaustively analyze all combinations of text transformations and their respective parameters to find out what common characteristics the best performing classifiers have. Furthermore, we introduce a novel approach based on the combination of word-based n-grams and character-based q-grams. The results show that this novel combination of words and characters produces a classifier that outperforms the traditional wordbased combination by 11.17% and 5.62% on the INEGI and TASS’15 dataset, respectively.Elsevier2017-09info:eu-repo/date/embargoEnd/2019-10-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243Expert Systems with Applications Volume 81, 15 September 2017, Pages 457-471reponame:Repositorio Institucional Centro GEOinstname:Centro de Investigación en Ciencias de Información Geoespacialinstacron:CENTROGEOenginfo:eu-repo/semantics/altIdentifier/DOI/https://doi.org/10.1016/j.eswa.2017.03.071info:eu-repo/semantics/embargoedAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:centrogeo.repositorioinstitucional.mx:1012/2432024-10-11T19:16:55Z
dc.title.none.fl_str_mv A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
title A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
spellingShingle A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
Oscar Sánchez Siordia
info:eu-repo/classification/Autor/Sentiment Analysis
info:eu-repo/classification/Autor/Error-robust text representations
info:eu-repo/classification/Autor/Opinion mining
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/33
info:eu-repo/classification/cti/3304
info:eu-repo/classification/cti/120304
info:eu-repo/classification/cti/120304
title_short A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
title_full A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
title_fullStr A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
title_full_unstemmed A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
title_sort A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
dc.creator.none.fl_str_mv Oscar Sánchez Siordia
Eric Tellez
SABINO MIRANDA JIMENEZ
Mario Graff
Daniela Moctezuma
Elio Atenógenes Villaseñor García
author Oscar Sánchez Siordia
author_facet Oscar Sánchez Siordia
Eric Tellez
SABINO MIRANDA JIMENEZ
Mario Graff
Daniela Moctezuma
Elio Atenógenes Villaseñor García
author_role author
author2 Eric Tellez
SABINO MIRANDA JIMENEZ
Mario Graff
Daniela Moctezuma
Elio Atenógenes Villaseñor García
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv info:eu-repo/classification/Autor/Sentiment Analysis
info:eu-repo/classification/Autor/Error-robust text representations
info:eu-repo/classification/Autor/Opinion mining
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/33
info:eu-repo/classification/cti/3304
info:eu-repo/classification/cti/120304
info:eu-repo/classification/cti/120304
topic info:eu-repo/classification/Autor/Sentiment Analysis
info:eu-repo/classification/Autor/Error-robust text representations
info:eu-repo/classification/Autor/Opinion mining
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/33
info:eu-repo/classification/cti/3304
info:eu-repo/classification/cti/120304
info:eu-repo/classification/cti/120304
description Sentiment analysis is a text mining task that determines the polarity of a given text, i.e., its positiveness or negativeness. Recently, it has received a lot of attention given the interest in opinion mining in micro-blogging platforms. These new forms of textual expressions present new challenges to analyze text because of the use of slang, orthographic and grammatical errors, among others. Along with these challenges, a practical sentiment classifier should be able to handle efficiently large workloads. The aim of this research is to identify in a large set of combinations which text transformations (lemmatization, stemming, entity removal, among others), tokenizers (e.g., word n-grams), and token-weighting schemes make the most impact on the accuracy of a classifier (Support Vector Machine) trained on two Spanish datasets. The methodology used is to exhaustively analyze all combinations of text transformations and their respective parameters to find out what common characteristics the best performing classifiers have. Furthermore, we introduce a novel approach based on the combination of word-based n-grams and character-based q-grams. The results show that this novel combination of words and characters produces a classifier that outperforms the traditional wordbased combination by 11.17% and 5.62% on the INEGI and TASS’15 dataset, respectively.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
info:eu-repo/date/embargoEnd/2019-10-16
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243
url http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/DOI/https://doi.org/10.1016/j.eswa.2017.03.071
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Expert Systems with Applications Volume 81, 15 September 2017, Pages 457-471
reponame:Repositorio Institucional Centro GEO
instname:Centro de Investigación en Ciencias de Información Geoespacial
instacron:CENTROGEO
instname_str Centro de Investigación en Ciencias de Información Geoespacial
instacron_str CENTROGEO
institution CENTROGEO
reponame_str Repositorio Institucional Centro GEO
collection Repositorio Institucional Centro GEO
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repository.mail.fl_str_mv
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