AUEB-ABSA at SemEval-2016 task 5: Ensembles of classifiers and embeddings for Aspect Based Sentiment Analysis

Abstract

This paper describes our submissions to the Aspect Based Sentiment Analysis task of SemEval-2016. For Aspect Category Detection (Subtask1/Slot1), we used multiple ensembles, based on Support Vector Machine classifiers. For Opinion Target Expression extraction (Subtask1/Slot2), we used a sequence labeling approach with Conditional Random Fields. For Polarity Detection (Subtask1/Slot3), we used an ensemble of two supervised classifiers, one based on hand crafted features and one based on word embeddings. Our systems were ranked in the top 6 positions in all the tasks we participated. The source code of our systems is publicly available.