abstract = "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 to
produce the final prediction. We analyzed EvoMSA on
different SA competitions to provide a global overview
of its performance. The results indicated that 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 aim was 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.",
notes = "Also known as \cite{8956106}
\cite{DBLP:journals/corr/abs-1812-02307}