  In the area of online communication, commerce and transactions, analyzing
sentiment polarity of texts written in various natural languages has become
crucial. While there have been a lot of contributions in resources and studies
for the English language, "smaller" languages like Czech have not received much
attention. In this survey, we explore the effectiveness of many existing
machine learning algorithms for sentiment analysis of Czech Facebook posts and
product reviews. We report the sets of optimal parameter values for each
algorithm and the scores in both datasets. We finally observe that support
vector machines are the best classifier and efforts to increase performance
even more with bagging, boosting or voting ensemble schemes fail to do so.
