The analysis of sentiment and emotion has become an important research topic in Natural Language Processing (NLP) due to the rapid growth of textual data generated on digital platforms. Still, despite significant progress, the existing literature remains fragmented across methods, modalities, and application domains, making it difficult to obtain a comprehensive understanding of current research trends. This study presents a structured literature review that synthesizes recent advances in sentiment and emotion analysis of textual data. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol and systematically examines studies retrieved from the Web of Science (WoS) and Scopus databases. After screening, eligibility evaluation, and Quality Assessment (QA), 50 primary studies published between 2023 and 2025 were selected for analysis. As such, the findings reveal a clear methodological transition from traditional Machine Learning (ML) techniques toward transformer-based architectures and Large Language Models (LLMs). In addition, recent studies increasingly explore multimodal approaches and context-aware emotion modeling to improve sentiment and emotion detection. Despite these advancements, several challenges remain, including the detection of implicit emotions, dataset imbalance, and domain adaptability. Overall, this review provides a structured synthesis of recent developments in textual sentiment and emotion analysis, identifies key research challenges, and outlines potential directions for future studies.
Ramli et al. (Thu,) studied this question.