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We present our supervised sentiment classification system which competed in SemEval2016 Task 4: Sentiment Analysis in Twitter. Our system employs a Support Vector Machine (SVM) classifier trained using a number of features including n-grams, synset expansions, various sentiment scores, word clusters, and term centroids. Using weighted SVMs, to address the issue of class imbalance, our system obtains positive class F-scores of 0.694 and 0.650, and negative class F-scores of 0.391 and 0.493 over the training and test sets, respectively.

DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons