This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity classification, tweet classification according to a two-point scale, and tweet classification according to a five-point scale. Our system is based on an ensemble consisting of Random Forests, SVMs, and Gradient Boosting Trees, and involves the use of a wide range of features including: ngrams,

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... rown clustering, sentiment lexicons, Wordnet, and part-of-speech tagging. The proposed system achieved 14 th , 6 th , and 3 rd place in subtasks A, B, and C, respectively.