Consumer sentiment analysis with aspect fusion and GAN-BERT aided adversarial learning

The sentiment analysis (SA) from the user-generated reviews is the latest research topic in natural language processing. Nowadays, the extraction of consumer sentiment from the content of the consumer reviews is getting much attention because of its importance in understanding consumers' experiences regarding services or products. Consumer sentiment may be helpful for both consumers and organizations; a consumer can refer to previous consumers' feedback while making their purchase decisions, and organizations can use it in service improvements. For the consumer SA, this article proposed the BERT-GAN model with review aspect fusion, which improves the fine-tuning performance of the BERT model by introducing semi-supervised adversarial learning. For our objective, we extracted various service aspects from consumer reviews and fused them with the word sequences before feeding them into the model. That helps in incorporating aspect representation as well as position information in context with the sentences. Our results analysis and their demonstration show the contribution of the presented model in terms of accuracy compared with the existing models found in the previous work.