This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Message Polarity Classification), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised approach based on the extraction of features by a dependency parsing-based approach using a sentiment lexicon and Natural Language Processing techniques.
['Jonathan Juncal-Martínez', 'Tamara Álvarez-López', 'Milagros Fernández Gavilanes', 'Enrique Costa-Montenegro', 'Francisco J. González-Castaño']
GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System