A study on LIWC categories for opinion mining in Spanish reviews

With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a u...

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Bibliographic Details
Published in:Journal of information science 2014-12, Vol.40 (6), p.749-760
Main Authors: del Pilar Salas-Zárate, María, López-López, Estanislao, Valencia-García, Rafael, Aussenac-Gilles, Nathalie, Almela, Ángela, Alor-Hernández, Giner
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Bayesian analysis
Blogs
Categories
Classification
Computation and Language
Computer Science
Data mining
Effectiveness studies
Exact sciences and technology
General aspects
Information and communication sciences
Information science. Documentation
Linguistics
Logic in Computer Science
Machine Learning
Natural language
Sciences and techniques of general use
Sentiment analysis
Social networks
Texts
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Summary:With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language are positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC (Linguistic Inquiry and Word Count). LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we conducted a comparative assessment of the performance of various classification techniques, J48, SMO and BayesNet, using precision, recall and F-measure metrics. The findings revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4 and 87.2% in each domain.
ISSN:0165-5515
1741-6485
DOI:10.1177/0165551514547842