  Sentiment analysis (SA) is a task related to understanding people's feelings
in written text; the starting point would be to identify the polarity level
(positive, neutral or negative) of a given text, moving on to identify emotions
or whether a text is humorous or not. This task has been the subject of several
research competitions in a number of languages, e.g., English, Spanish, and
Arabic, among others. In this contribution, we propose an SA system, namely
EvoMSA, that unifies our participating systems in various SA competitions,
making it domain independent and multilingual by processing text using only
language-independent techniques. EvoMSA is a classifier, based on Genetic
Programming, that works by combining the output of different text classifiers
and text models to produce the final prediction. We analyze EvoMSA on different
SA competitions to provide a global overview of its performance, and as the
results show, EvoMSA is competitive obtaining top rankings in several SA
competitions. Furthermore, we performed an analysis of EvoMSA's components to
measure their contribution to the performance; the idea is to facilitate a
practitioner or newcomer to implement a competitive SA classifier. Finally, it
is worth to mention that EvoMSA is available as open-source software.
