  Recently, sentiment analysis has received a lot of attention due to the
interest in mining opinions of social media users. Sentiment analysis consists
in determining the polarity of a given text, i.e., its degree of positiveness
or negativeness. Traditionally, Sentiment Analysis algorithms have been
tailored to a specific language given the complexity of having a number of
lexical variations and errors introduced by the people generating content. In
this contribution, our aim is to provide a simple to implement and easy to use
multilingual framework, that can serve as a baseline for sentiment analysis
contests, and as starting point to build new sentiment analysis systems. We
compare our approach in eight different languages, three of them have important
international contests, namely, SemEval (English), TASS (Spanish), and
SENTIPOLC (Italian). Within the competitions our approach reaches from medium
to high positions in the rankings; whereas in the remaining languages our
approach outperforms the reported results.
