Machine learning-based optimisation algorithms for sentiment analysis: an analysis of the state-of-the-art and upcoming challenges
Qiheng Sun, Yang Li, Guo Chen, Sangkeum Lee · Journal of Experimental & Theoretical Artificial Intelligence · 2025
The rapid growth of Internet-based applications, exemplified by the widespread adoption of social media platforms and weblogs, has started an era where comments and evaluations of everyday activities have become pervasive. In this context, sentiment analysis uses the capabilities of natural language processing, text analysis, and computational techniques to automate the extraction and classification of emotions and sentiments expressed in textual reviews. The insights derived from public opinions hold substantial value for businesses, governmental bodies, and individuals, serving as a critical source of information to support informed decision-making. Consequently, sentiment analysis has become a focal point within the field of decision support systems. Moreover, sentiment analysis based on machine learning (ML) has emerged as a cross-disciplinary approach to exploring opinion mining, particularly within the domains of media and communication research. These ML-based models have demonstrated great promise in achieving the goals of social media analysis due to their ability to capture both the structural subtleties and semantic complexities of text, without the need for the extensive feature engineering that characterised earlier approaches. While marketing researchers have employed various methods to analyse textual reviews, there remains a lack of a comprehensive performance evaluation framework, which complicates the selection of appropriate methods for future applications. This situation raises a fundamental question: What aspects are most frequently examined by researchers using ML-based sentiment analysis methods? To address this question, we propose a systematic literature review focused on sentiment analysis using ML techniques. This comprehensive review examines recent research efforts, highlighting the contributions of various scholars and focusing on ML techniques categorised into four primary clusters. The findings demonstrate the effectiveness of ML methodologies in conducting sentiment analysis with greater efficiency and accuracy. Notably, these ML models outperform their simpler counterparts, leading to a strong endorsement of their superiority.