Attention-Based Linguistically Constraints Network for Aspect-Level Sentiment

Abstract

Aspect-level sentiment analysis is an essential subtask of sentiment classification. It aims at classifying the sentiment polarity of given aspect in its context. Recently, a variety of deep learning models have been proposed to solve this task, such as Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks (CNN). In particular, great improvement has been achieved by using attention mechanism. At the same time, the adoption of linguistic resources to improve sentiment classification has also drawn researchers’ attention, and achieved state-of-the-art performance on traditional sentiment classification. Hence in this paper, we explore to combine linguistically constraints with attention mechanism to achieve comparable performance on aspect-level sentiment analysis. Experimental results on SemEval 2014 Datasets showed that the proposed model achieves good performance and verifies the effectiveness of linguistic resources on this task. To our knowledge, there are no work combining the attention mechanism and linguistic resources on this task before. This work gives inspirations to further research.