Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

  • María Pilar Salas-Zárate
  • Mario Andrés Paredes-Valverde
  • Miguel Ángel Rodríguez-García
  • Rafael Valencia-García
  • Giner Alor-Hernández
Chapter
Part of the Intelligent Systems Reference Library book series (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%.

Keywords

LIWC Machine learning Natural language processing Opinion mining Sentiment analysis 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • María Pilar Salas-Zárate
    • 1
  • Mario Andrés Paredes-Valverde
    • 1
  • Miguel Ángel Rodríguez-García
    • 2
  • Rafael Valencia-García
    • 1
  • Giner Alor-Hernández
    • 3
  1. 1.Departamento de Informática y SistemasUniversidad de MurciaMurciaSpain
  2. 2.Computational Bioscience Research CenterKing Abdullah University of Science and TechnologyThuwalKingdom of Saudi Arabia
  3. 3.Division of Research and Postgraduate StudiesInstituto Tecnológico de OrizabaOrizaba VeracruzMexico

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