Abstract. In this paper, we compare lexicon-based and machine learning-based approaches to define the subjectivity of tweets in Portuguese. We tested SentiLex and WordAffectBR lexicons, and Sequential Machine Optimization and Naive Bayes algorithms for this task. In our study, we used the Computer-BR corpus that contains messages about the technology area. We obtained better results using the Comprehensive Measurement Feature Selection method and the Sequential Machine Optimization algorithm as the classifier. We achieved considerable accuracy when we included the polarities of words in the vector space model of tweets.