Systematic Review of the Architectural Transition in Sentiment Analysis: From Hybrid Deep Learning to Generative Large Language Models
TL;DR
The findings show that although discriminative fine-tuned models continue to perform excellently in narrow classification tasks, generative LLMs possess impressive zero shot reasoning and flexibility but have issues with inconsistency and lack of transparency.
Abstract
The rapid evolution of natural language processing has driven sentiment analysis from simple lexicon based tools to sophisticated generative large language models. This systematic review charts that architectural transition, covering three distinct eras: rule based and statistical machine learning, hybrid deep learning architectures (notably CNN LSTM), and the current paradigm of transformer based and generative LLMs. Following PRISMA and Kitchenham guidelines, we synthesise findings from high impact publications (2020–2025) across IEEE, Elsevier, Springer, and ACL. The performance measures, contextual reasoning abilities, and other challenges like model variability, sarcasm detection, multimodal fusion, and interpretability form part of our analysis. Additionally, issues surrounding sustainability are addressed via Green AI techniques such as quantization and knowledge distillation. The findings show that although discriminative fine-tuned models continue to perform excellently in narrow classification tasks, generative LLMs possess impressive zero shot reasoning and flexibility but have issues with inconsistency and lack of transparency. We conclude by identifying key research gaps – deterministic benchmarking, uncertainty aware calibration, autonomous multimodal reasoning, and agentic explainability – and propose a roadmap for future work. This survey serves as a comprehensive resource for researchers and practitioners navigating the shifting landscape of sentiment analysis.