Deep Multi-Head Attention Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims to determine the sentiment of a specific aspect in the sentence. Most of the previous studies employ attention-based RNN models to capture aspect-dependent features in sentences or model Inter-Aspect Relation (IAR). However, RNN is difficult to parallelize when calculating all the elements in a sequence, and the word-level weight in attention mechanisms may introduce noise. Besides, we observe that the IAR contains inter-aspect syntactic relation and inter-aspect semantic relation, while the latter is overlooked in past IAR modeling studies. In this paper, we propose a new architecture that employs the multi-head attention mechanism to implement the parallel computation of sequence elements and introduce less noise than traditional attention mechanisms and model both relations in IAR. The experimental results on different types of data show that our model consistently outperforms state-of-the-art methods.