Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment
  Classification
Xueyi Liu Rui Hou Yanglei Gan Da Luo Changlin Li Xiaojun Shi Qiao Liu
Aspect-based sentiment classification is a crucial problem in fine-grained
sentiment analysis, which aims to predict the sentiment polarity of the given
aspect according to its context. Previous works have made remarkable progress
in leveraging attention mechanism to extract opinion words for different
aspects. However, a persistent challenge is the effective management of
semantic mismatches, which stem from attention mechanisms that fall short in
adequately aligning opinions words with their corresponding aspect in
multi-aspect sentences. To address this issue, we propose a novel
Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual
association between opinion words and the corresponding aspect. Specifically,
we first introduce a neighboring span enhanced module which highlights various
compositions of neighboring words and given aspects. In addition, we design a
multi-perspective attention mechanism that align relevant opinion information
with respect to the given aspect. Extensive experiments on three benchmark
datasets demonstrate that our model achieves state-of-the-art results. The
source code is available at https://github.com/AONE-NLP/ABSA-AOAN.