A Comprehensive Review of Opinion Mining: Methodological Innovations, Applications, and Emerging Challenges

Opinion mining, commonly referred to as sentiment analysis, has gained significant importance with the rapid growth of user-generated content across social media platforms, e-commerce websites, and online discussion forums.By automatically extracting opinions, emotions, and attitudes from textual data, opinion mining supports informed decision-making in domains such as business intelligence, governance, healthcare, and education.Recent advances in artificial intelligence, particularly deep learning, natural language processing (NLP), and large language models (LLMs), have substantially improved the accuracy and adaptability of sentiment analysis systems.Despite these advancements, several challenges remain unresolved.Contemporary models often struggle with sarcasm, contextual ambiguity, domain adaptation, and multilingual sentiment understanding.Additionally, the opaque nature of many deep learning and LLM-based approaches raises concerns regarding interpretability, fairness, and reliability, especially in high-stakes applications.Traditional sentiment models are also limited in capturing fine-grained and aspect-level opinions beyond coarse polarity classification.This review presents a comprehensive analysis of recent methodological innovations in opinion mining, including deep neural networks, hybrid architectures, graph-based models, prompt learning strategies, and large language models such as ChatGPT.The paper systematically examines their applications across diverse domains, including social media analytics, healthcare informatics, education, and software engineering.Furthermore, it identifies critical research gaps and emerging challenges related to explainable AI, low-resource and multilingual settings, and ethical deployment.By offering a structured taxonomy and comparative insights, this review aims to guide future research toward more robust, transparent, and scalable opinion mining systems.