An optimization-driven framework for sentiment classification in customer reviews addressing linguistic and rating bias via MOORA and transformer-based embeddings

Abstract

Sentiment analysis is a fundamental task in Natural Language Processing (NLP) that aims to automatically identify opinions and emotions expressed in textual data such as customer reviews, social media posts, and online feedback. However, sentiment classification remains challenging due to issues such as multilingual content, rating bias, informal language, and inconsistent relationships between review text and star ratings. For example, a review may contain positive text but be associated with a low rating, or include mixed sentiments within the same sentence, making accurate classification difficult. To address these challenges, this study proposes the Mathematical Optimization-Based Sentiment Tagging (MOST) framework, which integrates transformer-based language models with the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) technique for robust sentiment classification. In the proposed approach, contextual sentiment representations are extracted using SBERT, T5, and XLNet, and their probability scores are organized into a decision matrix where sentiment classes act as alternatives and model outputs serve as evaluation criteria. The MOORA optimization method is then applied to aggregate these scores and determine the final sentiment label. The proposed framework aims to answer the following research questions: (i) how transformer-based feature extraction combined with optimization techniques can improve sentiment classification accuracy, (ii) whether the model can remain robust across multilingual datasets, and (iii) how rating-independent sentiment tagging can mitigate rating bias. Experimental evaluation on multiple English and Hindi datasets demonstrates that the MOST model achieves up to 92–93% accuracy, outperforming several existing approaches while maintaining strong cross-domain and cross-language performance. These results highlight the effectiveness of integrating deep contextual embeddings with mathematical optimization for reliable sentiment analysis.