Graph-Based Attention Networks for Aspect Level Sentiment Analysis

Abstract

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we present a deep learning model that employs graph neural networks and graph-based attention mechanisms for aspect based sentiment analysis. In our work, the given text is considered as a graph based on its syntactic structure and the target is the specific region of the graph. Structural attention model and graph attention model are used to concentrate on relations between words and certain regions of the graph. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines. Code is available in supplementary materials.