Trustworthy Aspect-Based Opinion Mining through Confidence Calibration, Explainability, and Transformer Learning

One of the most important resources to learn about customer experiences, preferences and expectations is online reviews. Businesses, researchers, and policymakers increasingly rely on these reviews to gain insights into products and services. To address these challenges, this paper introduces a trust-aware and explainable aspect-based opinion mining system, named Trust-ABOM, for extracting opinions from online reviews. The framework is intended to highlight key aspects that are covered in a review, identify the sentiment expressed in each aspect, and quantify the degree of confidence of each sentiment prediction, and to present clear explanations of the findings. The proposed approach integrates reliability and confidence level evaluation with decision making process to give users an understanding and trust in the results produced by the system. The background not just boosts the accuracy of aspect-level opinion analysis, however it additionally supplies firms with trusted insights to help them make educated decisions. The results demonstrate that Trust-ABOM can interpret the opinions of the customers and provide explanations with satisfactory interpretability, which makes it a potential option for applications such as customer feedback analysis. Lastly, the proposed framework fosters the development of trustworthy and user-friendly AI systems that are correct and transparent in real-world opinion mining applications.