Fuzzy‐HAGRN: Fuzzy‐Based Hierarchical Attention Gated Recurrent Network for Sentiment Classification and Review Rate Prediction

Online reviews are essential for users to make purchasing decisions over any products or films. Reviews found on e‐commerce websites often closely align with ratings and enhance the learning experience for potential buyers. The customer review information is of huge interest to companies as well as customers. The information from customer feedback is not only valuable for consumers but also provides valuable feedback to companies. However, since customer reviews are usually presented as unstructured free text, extracting meaningful ratings from user opinions can be quite challenging. To overcome this issue, an effectual system is modeled for sentiment classification and review rating prediction utilizing reviews named fuzzy‐based hierarchical attention gated recurrent network (Fuzzy‐HAGRN). At first, the input reviews are fed to Bidirectional Transformers for Language Understanding (BERT) tokenization, and after that, feature extraction is executed for the extraction of several essential features. Then, Fuzzy‐HAGRN is utilized for sentiment classification and review rate prediction. Here, the Fuzzy‐HAGRN method is developed by integrating the fuzzy concept, hierarchical attention network (HAN) as well as gated recurrent network (GRU). Finally, rating prediction is performed using the same Fuzzy‐HAGRN. Fuzzy‐HAGRN has obtained a precision of 92.20%, recall of 91.80%, and F 1‐score of 91.50%.