many data types, meaning that it can produce the best (or close to best) results in almost all considered contexts, without any additional costs (e.g., manual labeling). Our self-learning approach is also very independent of the base methods, which means that it is highly extensible to incorporate any new additional method that can be envisioned in the future. Finally, we also investigate a transfer learning approach for sentiment analysis as a means to gather additional (unsupervised) information for the proposed approach and we show the potential of this technique to improve our results.'
author:
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    Philipe F. Melo (1), Daniel H. Dalip(2), Manoel M. Junior(1),\
    Marcos A. Gonçalves(1), Fabrício Benevenuto(1)\
    (1)Federal University of Minas Gerais, Brazil\
    (2)Centro Federal de Educação Tecnológica de Minas Gerais, Brazil\
title: '10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches '
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Introduction
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Online social media systems are places where people talk about everything, sharing their take or their opinions about noteworthy events. Not surprisingly, sentiment analysis has become an extremely popular tool in several analytic domains, but especially on social media data. The number of possible applications for sentiment analysis in this specific domain is growing fast. Many 