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This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where we participated in subtask A. Our system relies on AlchemyAPI and SentiWordNet to create 43 features based on which we select a feature subset as final representation. Active Learning then filters out noisy tweets from the provided training set, leaving a smaller set of only 900 tweets which we use for training a Multinomial Naive Bayes classifier to predict the labels of the test set with an F1 score of 0.478.

SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification