Abstract

This systematic review explores the integration of sentiment analysis and market research in shaping digital platform strategies. The paper synthesizes current literature on the tools, techniques, and applications of sentiment analysis, highlighting advancements in Natural Language Processing (NLP) and Machine Learning (ML) algorithms, such as lexicon-based approaches, deep learning models, and transformer-based architectures. It examines how these techniques contribute to strategic decisions related to customer feedback loops, brand perception, user engagement optimization, and market research methods such as A/B testing, surveys, and behavioral analytics. The review identifies key trends in the field, such as the rise of multimodal sentiment analysis and the need for more longitudinal studies. It also addresses the ethical considerations of data privacy, algorithmic bias, and the responsible use of insights. This paper offers significant implications for both practitioners and researchers, providing a comprehensive understanding of the current state of sentiment analysis and market research in digital platform strategy, while also suggesting areas for future inquiry and improvement.