Sentiment analysis datasets: a review of trends, challenges, and future opportunities

Sentiment Analysis (SA) is a core area within Natural Language Processing (NLP) that focuses on computationally identifying and interpreting subjective information. The performance and generalizability of SA models are closely linked to the characteristics and quality of the datasets used for training and evaluation. Although extensive research has advanced algorithmic techniques in SA, the literature offers comparatively limited consolidated analyses of the datasets that underpin benchmarking and empirical progress. To address this gap, this review provides an updated and comprehensive synthesis focused exclusively on SA datasets. We present a structured taxonomy based on labeling strategies, domain specificity, data sources, granularity levels, and modalities, enabling a clearer understanding of dataset typologies and their implications for SA research. The study analyzes key limitations in existing datasets and discusses emerging directions in dataset development, including multimodality, cross-lingual expansion, and synthetic data augmentation. Furthermore, we outline best practices for dataset annotation, validation, and benchmarking to support the development of more robust and transferable SA systems. By consolidating and examining the diverse landscape of SA datasets, this review aims to serve as a valuable resource for researchers and practitioners, offering guidance for informed dataset selection and laying a foundation for future advancements in SA dataset design and utilization.