Knowledge-enhanced neural networks for sentiment analysis of Chinese reviews

Abstract

Sentiment analysis aims to extract structured opinions from unstructured reviews and determine their sentiment polarities. However, existing sentiment analysis systems fail to identify aspect-opinion pairs and perform poorly on small training corpora. To address these issues, we propose a novel framework to model aspect-opinion pair identification and aspect-level sentiment classification as a joint text classification task. Moreover, we incorporate external knowledge into neural networks to compensate for the lack of training data. In our approach, context features extracted from review sentences and external knowledge retrieved from a sentiment knowledge graph are used to identify aspect-opinion pairs and determine their sentiment polarities. In this way, our model is able to provide more detailed sentiment analysis results and achieve better performance with limited training corpora. We evaluate our approach using a Chinese car review dataset. Experimental results show that the knowledge-enhanced neural networks consistently outperform the conventional models.