Enhancing Aspect Term Extraction from Customer Reviews with Sparse Gated Recurrent Units (SGRUs) in the Context of BERT and NER - Ziaulla, Mohammed; Biradar, Arun (International Journal of Intelligent Systems and Applications in Engineering, 2024)
A product evaluation system is a potent instrument that analyzes many online product reviews and recommends suitable products to consumers. A large amount of unlabeled data can be found on social networking sites. Fine-grained data annotation, on the other hand, is both costly and time-consuming. Aspect-based sentiment analysis (ABSA) aims to identify aspect terms in online reviews and predict their polarity. Sentiment analysis is a difficult and complex operation. Common subtasks include Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC). When these two subtasks are trained independently, the relationship between ATE and APC should be addressed. Extracting aspect phrases from customer evaluations is crucial for sentiment analysis and opinion mining. This work proposes a novel technique for improving aspect phrase extraction in the context of BERT (Bidirectional Encoder Representations from Transformers) and Named Entity Recognition (NER) by leveraging Sparse Gated Recurrent Units (SGRUs). In order to address the challenges of aspect word extraction, we provide a synergistic combination of cutting-edge methods such as Sparse GRUs, BERT, and NER. Sparse GRUs benefit from efficient computing and improved generalization by including sparsity requirements in the GRU architecture. This novel approach seeks to gather local and contextual information to improve the precision and relevance of derived aspect phrases. Our experimental results on a benchmark data set demonstrate the effectiveness of the proposed technique. By combining Sparse GRUs with BERT and NER, we can significantly improve the accuracy of aspect term extraction over earlier methods. The tests’ results suggest that Sparse GRUs can increase the identification of aspect phrases inside customer reviews, resulting in more accurate sentiment analysis and a better understanding of customer ratings.