  Sentiment analysis has become a very important tool for analysis of social
media data. There are several methods developed for this research field, many
of them working very differently from each other, covering distinct aspects of
the problem and disparate strategies. Despite the large number of existent
techniques, there is no single one which fits well in all cases or for all data
sources. Supervised approaches may be able to adapt to specific situations but
they require manually labeled training, which is very cumbersome and expensive
to acquire, mainly for a new application. In this context, in here, we propose
to combine several very popular and effective state-of-the-practice sentiment
analysis methods, by means of an unsupervised bootstrapped strategy for
polarity classification. One of our main goals is to reduce the large
variability (lack of stability) of the unsupervised methods across different
domains (datasets). Our solution was thoroughly tested considering thirteen
different datasets in several domains such as opinions, comments, and social
media. The experimental results demonstrate that our combined method (aka,
10SENT) improves the effectiveness of the classification task, but more
importantly, it solves a key problem in the field. It is consistently among the
best methods in 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.
