Sentiment Analysis of Portuguese Comments from Foursquare - Almeida, Thais G.; Souza, Bruno A.; Menezes, Alice A. F.; Figueiredo, Carlos Mauricio S.; Nakamura, Eduardo Freire (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2016: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, 2016)
Sentiment Analysis is an emerging research area applied to social media to find useful information from specific topics, such as service or product quality, or even general contexts as marketing, politics and economy. Although there are numerous studies using Sentiment Analysis, few of them address the Portuguese language, because of the language complexity. This study aims to evaluate the performance of three methods of machine learning (Support Vector Machine, Multinomial Naive Bayes, and Maximum Entropy) to detect polarities in two different scenarios. The first scenario is characterized as a three-class problem (positive, negative, and neutral). The second scenario consists of only two classes (positive and negative). Our dataset consists of comments from the Foursquare social network. The results show that Support Vector Machine presents an F-score at least 1.9% higher than the others for the three-class scenario, while Multinomial Naive Bayes presents an F-score at least 2.6% higher than the others for two-class scenario.