A weakly-supervised graph-based joint sentiment topic model for multi-topic sentiment analysis

Multi-topic sentiment analysis, which aims to identify the topics and classify their corresponding sentiment, is of great value in understanding consumers’ behaviour and improving services. Because of the high cost of manual annotation of the datasets, topic model-based approaches that model the joint distributions of both topics and sentiments have been studied previously. Some studies proposed models that leverage the prior knowledge derived from the pre-trained word embeddings and have proven effective. However, most of the existing models are based on the assumption that words and topics are conditionally independent, ignoring the dependency relations among them. Additionally, the fine-tuning of the pre-trained word embeddings to incorporate the contextual information is also neglected in these models. This could result in the ambiguous representations of topics. In this paper, we propose a novel weakly-supervised graph-based joint sentiment topic model (W-GJST) that integrates an edge-gated graph convolutional network (E-GCN) into a joint sentiment-topic model. An importance sampling-based training method is proposed to learn the contextual representations of topics and words efficiently. Additionally, a self-training multi-topic classifier is designed for the multi-label topic identification. Experiments on two benchmark datasets demonstrate the superiority of the proposed W-GJST compared to the baseline models in terms of topic modelling, topic identification and topic-sentiment identification.