CGPA-UGRCA: A Novel Explainable AI Model for Sentiment Classification and Star Rating Using Nature-Inspired Optimization

Amit Kumar Srivastava, Pooja (), Musrrat Ali () and Yonis Gulzar
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Amit Kumar Srivastava: Department of Electronics and Communication, University of Allahabad, Prayagraj 211002, India
Pooja: Department of Electronics and Communication, University of Allahabad, Prayagraj 211002, India
Musrrat Ali: Department of Mathematics and Statistics, College of Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Mathematics, 2025, vol. 13, issue 22, 1-29

Abstract: In recent years, social media-related sentiment classification has been researched extensively and is applied in various fields such as opinion mining, commodity feedback, and market analysis. Therefore, it is important to understand and analyse the opinions of the public, their feedback, and data related to social media. Consumers continue to face challenges in accessing review-based sentiment classification expressed by their peers, and the existing method does not provide satisfactory results. Hence, an innovative sentiment classification method, the Convoluted Graph Pyramid Attention (CGPA) model, combined with the Updated Greater Cane Rat Algorithm (UGCRA), is proposed. This method improves sentiment classification by optimizing accuracy and efficiency while addressing inherent uncertainties, allowing for precise sentiment intensity evaluation across multiple dimensions. Explainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAPs), enhance the model’s transparency and interpretability. This approach enables the final ranking of classified reviews, predicts ratings on a scale of one to five stars, and generates a recommendation list based on the predicted user ratings. Comparison between other traditional existing methods and the result indicates that the proposed method achieves superior performance. From the experimental results, the proposed approach achieves an accuracy of 99.5% in the Restaurant Review dataset, 99.8% in the Edmund Consumer Car Ratings Reviews dataset, 99.9% in the Flipkart Cell Phone Reviews dataset, and 99.7% in the IMDB Movie database, showing its effectiveness in analysing sentiments with an increase in performance.

Keywords: sentiment classification; graph pyramid attention; greater cane rat; interpretability; recommendation list; five-star rating (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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