A Comprehensive Survey on Reliable Sentiment Analysis: Models, Datasets, and Future Directions

Abstract

Over the past decade, sentiment analysis has evolved from traditional rule-based and lexicon-driven approaches to advanced deep learning and transformer-based architectures capable of contextual and cross-lingual understanding. These advancements have expanded its applications across domains such as social media analytics, customer experience management, finance, and healthcare. However, challenges related to reliability, generalization, interpretability, and fairness remain significant. This study presents a comprehensive review of sentiment analysis from multiple perspectives, including datasets, modeling techniques, application domains, and evaluation frameworks. The paper examines traditional machine learning methods, deep learning architectures, and transformer-based models, highlighting their methodological foundations, advantages, and limitations. Particular attention is given to issues surrounding dataset quality, annotation strategies, ethical data collection, and bias in sentiment datasets. In addition, the study discusses critical challenges such as domain adaptation, multilingual sentiment analysis, sarcasm detection, and real-world deployment constraints. Emerging research directions and future opportunities are also outlined to support the development of more robust, interpretable, and ethically aligned sentiment analysis systems. Overall, this work provides a structured overview of current advancements and research gaps, serving as a reference framework for researchers and practitioners working on reliable sentiment analysis in natural language processing.