Comitê de classificador para mineração de opinião de eleitores brasileiros

Abstract

The recent and exponential growth of social media and user-generated content on the Internet, provide a wealth of data to uncover the feelings, opinions, and experiences of users and customers. Among the various social media currently available, Twitter stands out, with more than 336 million monthly active users. Understanding what people are thinking or their opinions is critical to decision making, especially in the context in which these people express their comments voluntarily. For this type of analysis, opinion mining is used, also known as sentiment analysis, which analyzes people’s opinions, feelings and emotions about entities such as services, products, brands, individuals, problems, events and topics, be expressed in an unstructured form, as in texts, for example. The spread of social media also raises the possibility of delving deeper into the web to explore and track citizens’ political and electoral preferences. In this sense, recent studies explore social media as a device to assess the popularity of politicians during the campaign. Analyzing social media during an election campaign can be a useful supplement or supplement to traditional surveys, and among the reasons include the low cost and speed of real-time analysis. This paper aims to analyze the efficiency of a ensemble for opinion mining applications, using data extracted from Brazilian voters. In this study, an application of opinion mining was developed at the document level using ensembles, with the objective of analyzing the efficiency of the ensembles in such applications. For that, data extracted from Twitter containing opinions of Brazilian voters were used. Six pre-processing configurations and three ensemble approaches were evaluated: Voting, Bagging and Boosting. The results were obtained in terms of Accuracy. According to the experiments, configuration 3, which uses stopwords, n-gram and TF-IDF, achieved the best results, with a 90.26% accuracy for the committee that used the Boosting approach.