Fine-Grained Sentiment Analysis: Leveraging BERT for Aspect-Level Customer Feedback Classification
TL;DR
The proposed framework illustrates how well the BERT-based ABSA model accurately identifies and evaluates various aspects of goods or services, as indicated in customer feedback, and adds value to the body of current sentiment analysis literature, suggesting useful recommendations for improving the explanation and understanding of customer feedback.
Abstract
Organizations can now tap into fine-grained opinions about products or service features that can be extracted from customer reviews and ratings with aspect-based sentiment analysis (ABSA). The study introduced two models, the first for aspect extraction and another one involved in sentiment analysis using bidirectional encoder representation transformer (BERT). With a testing accuracy of 98%, the aspect extraction model produces outstanding results, corroborated by metrics for precision, recall, and F1-score for every class. Furthermore, the ABSA model gives remarkable progress over earlier research, attaining an 82% testing accuracy. The proposed framework illustrates how well the BERT-based ABSA model accurately identifies and evaluates various aspects of goods or services, as indicated in customer feedback, and adds value to the body of current sentiment analysis literature, suggesting useful recommendations for improving the explanation and understanding of customer feedback. There is a chance that this research project will help consumers and businesses alike.