Interactive Attention Networks for Aspect-Level Sentiment Classification

TL;DR

This paper introduces Interactive Attention Networks (IAN) that separately model targets and contexts with interactive learning, improving aspect-level sentiment classification accuracy.

Contribution

The paper proposes a novel IAN model that interactively learns separate representations for targets and contexts, enhancing sentiment analysis performance.

Findings

IAN outperforms previous models on SemEval 2014 datasets.

Separate modeling of targets and contexts improves sentiment classification.

Interactive learning effectively captures target-context relationships.

Abstract

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.