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    Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

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    Type
    Book Chapter
    Authors
    Salas-Zárate, María Pilar
    Paredes-Valverde, Mario Andrés
    Rodriguez-Garcia, Miguel Angel cc
    Valencia-García, Rafael cc
    Alor-Hernández, Giner
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-03-15
    Online Publication Date
    2017-03-15
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/623027
    
    Metadata
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    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%.
    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.
    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.
    Publisher
    Springer Nature
    Journal
    Current Trends on Knowledge-Based Systems
    DOI
    10.1007/978-3-319-51905-0_4
    Additional Links
    http://link.springer.com/chapter/10.1007/978-3-319-51905-0_4
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-51905-0_4
    Scopus Count
    Collections
    Computational Bioscience Research Center (CBRC); Book Chapters; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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