Aspect-Based Sentiment Analysis Model with Multiple Grammatical Information

Abstract

The aim of aspect based sentiment analysis (ABSA) is to classify the sentiment polarity towards a particular aspect in a sentence. Existing approaches usually apply attention mechanism to modeling the connection between aspects and opinion expression in an implicit way. However, they ignore the potentially useful grammatical information. On one hand, the sentiment polarity of an aspect is closely related to opinion expression, and syntactic information helps to better model the relation of them. On the other hand, models are hard to learn general grammatical knowledge when trained on existing small benchmark datasets, resulting in difficulty to handle complex sentence patterns and opinion expressions. To address the problem, this paper proposes a neural network that combines various kinds of grammatical information to enhance the accuracy. This paper employs dependency tree based graph convolutional networks (GCN) to match aspects and their corresponding opinion expression directly using syntactic information and eliminate useless information. This paper also uses middle layers of BERT as guiding information to enhance the model, which contains various kinds of contextual and grammatical information. The input of each GCN layer fuses the output of the preceding GCN layer and BERT (bidirectional encoder representations from transformers) middle layers. Finally, the aspect representation of the last layer GCN is used as the feature to identify sentiment polarity. Experiments on SemEval 2014 Task4 Restaurant, Laptop and Twitter show the model outperforms other baselines.

Keywords

• |aspect|sentiment analysis|bidirectional encoder representations from transformers (bert)|dependency tree|graph convolutional networks (gcn)