Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12

This paper describes the participation in SemEval-2015 Task 12, and the opinion mining system sentiue, which used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features.

Abstract

This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue.The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity.For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain.The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition.In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features.Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%.In sentiment polarity, sentiue's result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains.