This paper describes the system implemented by Fundaci´ o Barcelona Media (FBM) for classifying the polarity of opinion expressions in tweets and SMSs, and which is supported by a UIMA pipeline for rich linguistic and sentiment annotations. FBM participated in the SEMEVAL 2013 Task 2 on polarity classification. It ranked 5th in Task A (constrained track) using an ensemble system combining ML algorithms with dictionary-based heuristics, and 7th (Task B, constrained) using an SVM classifier with features derived from the linguistic annotations and some heuristics.
['Carlos Rodriguez-Penagos', 'Jordi Atserias Batalla', 'Joan Codina-Filbà', "David Garc'ia-Narbona", 'Jens Grivolla', 'Patrik Lambert', 'Roser Saurí']
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities