SZTE-NLP: Aspect level opinion mining exploiting syntactic cues
Viktor Hangya, Gábor Berend, István Varga, Richárd Farkas · 2014
In this paper, we introduce our contribu-tions to the SemEval-2014 Task 4 – As-pect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict classes. To solve this problem, we employed supervised ma-chine learning techniques exploiting a rich feature set. Our feature templates ex-ploited both phrase structure and depen-dency parses. 1