Web 2.0 applications for social networking provide data about users’ mood and opinions in almost real time. Many applications are taking advantage of these data to derive business intelligence. However, the volume of data makes it hard and error-prone to classify sentiments and opinions manually. The combination of data mining techniques and a pipeline to process data from Web 2.0 applications, such as Twitter, Facebook, and Wordpress, makes it possible to apply natural language processing and machine learning techniques to automate partially this task. Therefore, the amount of manual classification is reduced, as the incoming data has already a classification tag that can be easily changed, feeding back the classifier. There is room for improvements and a Brazilian Portuguese Corpus was created to do the initial training of the classifier. The code used for this testing was based on open source libraries and is available as a test bed for different corpora and new algorithms.
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