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.

本研究提出了一种基于依存句法分析树的注意力机制方法，以捕获与情感极性相关的方面位置信息，作为 Aspect based sentiment analysis (ABSA) 的一种当前先进的方法。