ATP: A holistic attention integrated approach to enhance ABSA

TL;DR

This paper introduces ATP, a novel attention mechanism that incorporates dependency parsing to improve aspect-based sentiment analysis by better capturing the importance of words relative to aspects.

Contribution

It proposes a dependency parsing tree-based position encoding to enhance attention mechanisms in deep learning models for ABSA, outperforming simple distance-based methods.

Findings

Improved sentiment classification accuracy on SemEval'14 dataset.

Dependency parsing-based attention outperforms traditional word-distance approaches.

Demonstrated effectiveness of holistic attention in ABSA tasks.

Abstract

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA.