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. The code
can be found at
https://github.com/balikasg/SemEval2016-Twitter_Sentiment_Evaluation.