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Current Trends on Knowledge-Based Systems pp 73–92Cite as

Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 120))

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%.

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Acknowledgements

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.

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Authors and Affiliations

  1. Departamento de Informática y Sistemas, Universidad de Murcia, Campus de Espinardo, 30100, Murcia, Spain

    María Pilar Salas-Zárate, Mario Andrés Paredes-Valverde & Rafael Valencia-García

  2. Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, P.O. Box 2882, 23955-6900, Thuwal, Kingdom of Saudi Arabia

    Miguel Ángel Rodríguez-García

  3. Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Av. Oriente 9 no. 852 Col. E. Zapata, CP 94320, Orizaba Veracruz, Mexico

    Giner Alor-Hernández

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  1. María Pilar Salas-Zárate
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  3. Miguel Ángel Rodríguez-García
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  4. Rafael Valencia-García
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Correspondence to María Pilar Salas-Zárate .

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  1. 852 Col. Emiliano Zapata, Instituto Tecnologico de Orizaba 852 Col. Emiliano Zapata, Orizaba, Veracruz, Mexico

    Giner Alor-Hernández

  2. Campus de Espinardo s/n., Facultad de Informática Campus de Espinardo s/n., Murcia, Spain

    Rafael Valencia-García

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Salas-Zárate, M.P., Paredes-Valverde, M.A., Rodríguez-García, M.Á., Valencia-García, R., Alor-Hernández, G. (2017). Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language. In: Alor-Hernández, G., Valencia-García, R. (eds) Current Trends on Knowledge-Based Systems. Intelligent Systems Reference Library, vol 120. Springer, Cham. https://doi.org/10.1007/978-3-319-51905-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-51905-0_4

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