Abstract
Opinion mining is the study of opinions and emotions of authors about specific topics on the Web. Opinion mining identifies whether the opinion about a given topic, expressed in a document, is positive or negative. Nowadays, with the exponential growth of social medial i.e. 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. This paper investigates technological products reviews mining using the psychological and linguistic features obtained through of text analysis software, LIWC. Furthermore, an analysis of the classification techniques J48, SMO, and BayesNet has been performed by using WEKA (Waikato Environment for Knowledge Analysis). This analysis aims to evaluate the classifying potential of the LIWC (Linguistic Inquiry and Word Count) dimensions on written opinions in Spanish. All in all, findings have revealed that the combination of the four LIWC dimensions provides better results than the other combinations and individual dimensions, and that SMO is the algorithm which has obtained the best results.Keywordssentiment analysisopinion miningLIWCmachine learning
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