Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis problem, which has attracted much attention in recent years. Previous methods mainly devote to employing attention mechanism to model the relationship between aspects and context words. However, these methods tend to ignore the overall semantics of sentence and dependency among the aspect terms. In this paper, we propose a Hierarchical Attention Network (HAN) to solve the aforementioned issues simultaneously. Experimental results on standard SemEval 2014 datasets demonstrate the effectiveness of the proposed model.