bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis

Abstract

This paper describes a sentiment analysis system developed by the bunji team in SemEval-2016 Task 5. In this task, we estimate the sentimental polarity of a given entity-attribute (E#A) pair in a sentence. Our approach is to estimate the relationship between target entities and sentimental expressions. We use two different methods to estimate the relationship. The first one is based on a neural attention model that learns relations between tokens and E#A pairs through backpropagation. The second one is based on a rule-based system that examines several verb-centric relations related to E#A pairs. We confirmed the effectiveness of the proposed methods in a target estimation task and a polarity estimation task in the restaurant domain, while our overall ranks were modest.