  This paper describes the participation of the team "TwiSE" in the SemEval
2016 challenge. Specifically, we participated in Task 4, namely "Sentiment
Analysis in Twitter" for which we implemented sentiment classification systems
for subtasks A, B, C and D. Our approach consists of two steps. In the first
step, we generate and validate diverse feature sets for twitter sentiment
evaluation, inspired by the work of participants of previous editions of such
challenges. In the second step, we focus on the optimization of the evaluation
measures of the different subtasks. To this end, we examine different learning
strategies by validating them on the data provided by the task organisers. For
our final submissions we used an ensemble learning approach (stacked
generalization) for Subtask A and single linear models for the rest of the
subtasks. In the official leaderboard we were ranked 9/35, 8/19, 1/11 and 2/14
for subtasks A, B, C and D respectively.\footnote{We make the code available
for research purposes at
\url{https://github.com/balikasg/SemEval2016-Twitter\_Sentiment\_Evaluation}.}
