Affective Knowledge Augmented Interactive Graph Convolutional Network for Chinese-Oriented Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis(ABSA) aims to identify the sentiment polarity of specific aspects in sentences, which can more accurately mine the sentiment polarity of users towards different aspects. Most of the existing works derive the sentiment features of specific aspects by interactively learning the dependencies between different aspects of the context. However, the above work has neglected to use the external affective commonsense knowledge to augment the ability of the Graph Convolutional Networks(GCNs) to interactively capture sentiment dependencies of the inter-aspect words in different contexts. In addition, compared to the ABSA research in English, the existing research pays less attention to the Chinese-oriented research. Meanwhile, multi-head self-sttention(MHSA) is applied to extract richer context syntax and semantic interaction features. In this paper, we propose a novel knowledge-aware model in which affective knowledge augments interactive GCN for Chinese-oriented ABSA, namely AKM-IGCN. Moreover, this model can be applied to effectively analyze both Chinese and English comments simultaneously. Hence, we conducted experiments on four Chinese datasets(Camera, Phone, Notebook and Car) and six English benchmark datasets(Restaurant14, Restaurant15, Restaurant16, Twitter, MAMS, Tshirt). Experimental results illustrate that our proposed model outperforms or approaches state-of-the-art models.