Abstract
This paper presents the approach of the GTI Research Group to SemEval-2015 task 10 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Contextual Polarity Disambiguation) and B (Message Polarity Classification). We followed an unsupervised dependency parsing-based approach using a sentiment lexicon, created by means of an automatic polarity expansion algorithm and Natural Language Processing techniques. These techniques involve the use of linguistic peculiarities, such as the detection of polarity conflicts or adversative/concessive subordinate clauses. The results obtained confirm the competitive and robust performance of the system.
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