FBM: Combining lexicon-based ML and heuristics for Social Media Polarities
Carlos Gerardo Rodriguez-Penagos, Jordi Atserias, Joan Codina, David Garc'ia-Narbona, Jens Grivolla, Patrik Lambert, R. Supyan Sauri · 2013
This paper describes the system implemented by Fundació 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. 1