Mineração de Texto para a Análise do Perfil Emocional de Usuários de Jogo Empático

Fabíola Pantoja Oliveira Araújo, Leonardo Dias Martins

Abstract

Daily, a large amount of data circulates on the Internet, producing a lot of information in the form of images, videos and texts. Then, it is necessary to analyze and extract these information automatically. Therefore, this work presents a case study that applies text mining to extract the emotional and sentimental profiles from the comments of the Last Day of June game users, where the results and the information extracted from the analysis of sentiments were presented. Three classification algorithms were used: Naive Bayes, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to predict the class of elements according to the emotions or feelings identified in the comments analysis. As a result, SVM with radial kernel was the one with the best accuracy, with 79%, followed by KNN with 3 closest neighbors, with 75%, and finally, Naive Bayes, with 62%.

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