This article analyzes Brazilian Consumers' Sentiments in a specific domain using a system, SentiMeter-Br. A Portuguese dictionary focused in a specific field of study was built, in which tenses and negative words are treated in a different way to measure the polarity, the strength of positive or negative sentiment, in short texts extracted from Twitter. For the Portuguese Dictionary performance validation, the results are compared with the SentiStrength tool and are evaluated by three Specialists in the field of study, each one analyzed 2000 texts captured from Twitter. Comparing the efficiency of the SentiMeter-Br and the SentiStrength against the Specialists' opinion, a Pearson correlation factor of 0.89 and 0.75 was reached, respectively, proving that the metric used in the Sentimeter-Br is better than the one used in the SentiStrength. The polarity of the short texts were also tested through machine learning, with correctly classified instances of 71.79% by Sequential Minimal Optimization algorithm and F-Measure of 0.87 for positive and 0.91 for negative phrases. Another contribution is a Twitter and Facebook search framework that extracts online tweets and Facebook posts, the latter with geographic location, gender and birth date of the user who posted the comments, and can be accessed by mobile phones. © 2013 IEEE.