Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

Handle URI:
http://hdl.handle.net/10754/623027
Title:
Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language
Authors:
Salas-Zárate, María Pilar; Paredes-Valverde, Mario Andrés; Rodriguez-Garcia, Miguel Angel ( 0000-0001-6244-6532 ) ; Valencia-García, Rafael; Alor-Hernández, Giner
Abstract:
Recent research activities in the areas of opinion mining, sentiment analysis and emotion detection from natural language texts are gaining ground under the umbrella of affective computing. Nowadays, there is a huge amount of text data available in the Social Media (e.g. forums, blogs, and social networks) concerning to users’ opinions about experiences buying products and hiring services. Sentiment analysis or opinion mining is the field of study that analyses people’s opinions and mood from written text available on the Web. In this paper, we present extensive experiments to evaluate the effectiveness of the psychological and linguistic features for sentiment classification. To this purpose, we have used four psycholinguistic dimensions obtained from LIWC, and one stylometric dimension obtained from WordSmith, for the subsequent training of the SVM, Naïve Bayes, and J48 algorithms. Also, we create a corpus of tourist reviews from the travel website TripAdvisor. The findings reveal that the stylometric dimension is quite feasible for sentiment classification. Finally, with regard to the classifiers, SVM provides better results than Naïve Bayes and J48 with an F-measure rate of 90.8%.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Salas-Zárate MP, Paredes-Valverde MA, Rodríguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language. Intelligent Systems Reference Library: 73–92. Available: http://dx.doi.org/10.1007/978-3-319-51905-0_4.
Publisher:
Springer Nature
Journal:
Current Trends on Knowledge-Based Systems
Issue Date:
14-Mar-2017
DOI:
10.1007/978-3-319-51905-0_4
Type:
Book Chapter
ISSN:
1868-4394; 1868-4408
Sponsors:
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R). María Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), and the Mexican government.
Additional Links:
http://link.springer.com/chapter/10.1007/978-3-319-51905-0_4
Appears in Collections:
Computational Bioscience Research Center (CBRC); Book Chapters

Full metadata record

DC FieldValue Language
dc.contributor.authorSalas-Zárate, María Pilaren
dc.contributor.authorParedes-Valverde, Mario Andrésen
dc.contributor.authorRodriguez-Garcia, Miguel Angelen
dc.contributor.authorValencia-García, Rafaelen
dc.contributor.authorAlor-Hernández, Gineren
dc.date.accessioned2017-03-20T07:50:10Z-
dc.date.available2017-03-20T07:50:10Z-
dc.date.issued2017-03-14en
dc.identifier.citationSalas-Zárate MP, Paredes-Valverde MA, Rodríguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language. Intelligent Systems Reference Library: 73–92. Available: http://dx.doi.org/10.1007/978-3-319-51905-0_4.en
dc.identifier.issn1868-4394en
dc.identifier.issn1868-4408en
dc.identifier.doi10.1007/978-3-319-51905-0_4en
dc.identifier.urihttp://hdl.handle.net/10754/623027-
dc.description.abstractRecent research activities in the areas of opinion mining, sentiment analysis and emotion detection from natural language texts are gaining ground under the umbrella of affective computing. Nowadays, there is a huge amount of text data available in the Social Media (e.g. forums, blogs, and social networks) concerning to users’ opinions about experiences buying products and hiring services. Sentiment analysis or opinion mining is the field of study that analyses people’s opinions and mood from written text available on the Web. In this paper, we present extensive experiments to evaluate the effectiveness of the psychological and linguistic features for sentiment classification. To this purpose, we have used four psycholinguistic dimensions obtained from LIWC, and one stylometric dimension obtained from WordSmith, for the subsequent training of the SVM, Naïve Bayes, and J48 algorithms. Also, we create a corpus of tourist reviews from the travel website TripAdvisor. The findings reveal that the stylometric dimension is quite feasible for sentiment classification. Finally, with regard to the classifiers, SVM provides better results than Naïve Bayes and J48 with an F-measure rate of 90.8%.en
dc.description.sponsorshipThis work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R). María Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), and the Mexican government.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/chapter/10.1007/978-3-319-51905-0_4en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-51905-0_4en
dc.subjectLIWCen
dc.subjectMachine learningen
dc.subjectNatural language processingen
dc.subjectOpinion miningen
dc.subjectSentiment analysisen
dc.titleSentiment Analysis Based on Psychological and Linguistic Features for Spanish Languageen
dc.typeBook Chapteren
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalCurrent Trends on Knowledge-Based Systemsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartamento de Informática y Sistemas, Universidad de Murcia, Campus de Espinardo, 30100, Murcia, Spainen
dc.contributor.institutionDivision of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Av. Oriente 9 no. 852 Col. E. Zapata, CP 94320, Orizaba Veracruz, Mexicoen
kaust.authorRodriguez-Garcia, Miguel Angelen
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