Attentional Interactive Encoder Network Focused on Aspect for Sentiment Classification

Abstract

Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than syntactic dependency features, and paid too much attention to the context words, while ignoring the high-level interaction of multiple representations of aspects themselves. To cope with these limitations, we propose a new method based on the graph convolutional network (GCN) and the multi-head attention mechanism, called the attention interactive encoder network (AIEN). The GCN was used to obtain the syntactic information that has the greatest syntactic impact on the aspect based on the syntax dependency tree. The multi-head attention mechanism can not only obtain the context-aware information of the given aspects, but also the interaction information between multiple representations of the aspect itself. The high-level information generated by the interaction of multi-dimensional features can produce a stronger representation ability for the aspect. Our experiments with the proposed model on five benchmark datasets showed that our model outperformed other works significantly. The experimental results further demonstrated the feasibility and applicability of our proposed model in the ABSA task.

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

“…• SGGCN + BERT [56]: Alters the graph-based model's hidden vectors to make the most of information from the aspects. • AIEN + BERT [57]: Constructs an interaction encoder using a GCN and attention mechanisms for extracting interaction features. • KHGCN: Utilizes a dynamic weighting mechanism to acquire word-level embeddings during the encoding phase.…”