DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter

Abstract

We present our supervised sentiment classification system which competed in SemEval-2015 Task 10B: Sentiment Classification in Twitter- Message Polarity Classification. Our system employs a Support Vector Machine classifier trained using a number of features including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. Using weighted Support Vector Machines, to address the issue of class imbalance, our system obtains positive class F-scores of 0.701 and 0.656, and negative class F-scores of 0.515 and 0.478 over the training and test sets, respectively.