Sentiment Analysis in the Age of Transformers and Large Language Models: A Comprehensive Review of Recent Advances and Future - Amrullah, Ata (Intellithings Journal, 2025)
Sentiment analysis (SA) has undergone a significant transformation with the emergence of Transformer-based models and Large Language Models (LLMs). This review provides a comprehensive overview of recent advances in Transformer-based SA, highlighting the impact of LLMs on accuracy, nuance, and context-awareness. Architectural innovations, domain adaptation techniques, and methods for handling context and long-range dependencies are explored. The review also addresses the critical challenges and limitations associated with LLMs in SA, including bias and fairness, interpretability and explainability, data scarcity, computational cost, and robustness against adversarial attacks. Mitigation strategies and best practices are examined, focusing on data augmentation, adversarial training, bias-aware training objectives, attention visualization, and model distillation. Finally, the review outlines future research directions, emphasizing multimodal sentiment analysis, explainable AI, ethical considerations, low-resource languages, and domain-specific applications. This work concludes that while LLMs offer unprecedented opportunities for advancing SA, addressing the identified challenges is crucial for ensuring responsible and effective deployment in real-world scenarios.