Paper: SZTE-NLP: Aspect level opinion mining exploiting syntactic cues

ACL ID S14-2107
Title SZTE-NLP: Aspect level opinion mining exploiting syntactic cues
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

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.