Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

María Pilar Salas-Zárate , Mario Andrés Paredes-Valverde , Miguel Ángel Rodríguez-García , Rafael Valencia-García , Giner Alor-Hernández

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%. More... »

PAGES

73-92

References to SciGraph publications

  • 2015. Sentiment Analysis on Twitter Streaming Data in EMERGING ICT FOR BRIDGING THE FUTURE - PROCEEDINGS OF THE 49TH ANNUAL CONVENTION OF THE COMPUTER SOCIETY OF INDIA (CSI) VOLUME 1
  • 2015. Modeling Indian General Elections: Sentiment Analysis of Political Twitter Data in INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS
  • 2014. Sentence-Level Sentiment Analysis in the Presence of Modalities in COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING
  • 2015. Simple Approaches of Sentiment Analysis via Ensemble Learning in INFORMATION SCIENCE AND APPLICATIONS
  • 2013. Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • Book

    TITLE

    Current Trends on Knowledge-Based Systems

    ISBN

    978-3-319-51904-3
    978-3-319-51905-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-51905-0_4

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    http://dx.doi.org/10.1007/978-3-319-51905-0_4

    DIMENSIONS

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