DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach

This paper contains the description of our participation at task 4 (sub-task A, Message Polarity Classification) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the preprocessing step includes the tokenization and identification of special elements including URLs, hashtags, user mentions and emoticons. The second step aims at selecting and extracting the feature set. Finally, a supervised approach, in particular a Support Vector Machine has been applied to tackle the classification problem.