FuzzySentClass: Interval-Valued Fuzzy Approach to the Sentiment Analysis Problem via SentiWordNet

Sentiment analysis, especially social network analysis (SNA), is a relevant research area that consists of analyzing and extracting emotions, opinions, or attitudes from reviews of products, services, music, and movies, classifying them into positive, neutral, and negative. In this article, we propose a new approach using the Interval-valued Fuzzy Logic called FuzzySentClass to classify tweets based on lexicon using SentiWordnet. The obtained results are evaluated based on the accuracy of the classifications obtained in the executions varying the type reducer in the Defuzzification step of FuzzySentClass. In addition, the interval entropy approach is used to measure the imprecision information of achieved results. Our approach reached an accuracy of 83.22 % with the centroid type reducer and 82.63 % with the center of sets type reducer. And, this results in the values of 0.117469 and 0.149853 as the maximum diameter of interval entropy for IvFS related to input and output variables, respectively.