This chapter explores sentiment analysis (SA) as a powerful methodological approach for advancing STEM education research through data-driven insights. By systematically examining students’ and teachers’ emotions, opinions, and attitudes expressed in textual data, SA offers meaningful insights into learning experiences, engagement, and instructional practices. The chapter outlines three main approaches to SA—lexicon-based, artificial intelligence (AI)-based, and hybrid models—and details their implementation through traditional machine learning, deep learning, and large language models (LLMs). Two use cases demonstrate the practical application of SA: one analyzing students’ feedback on online learning and the other exploring educators’ attitudes toward ChatGPT in education. These examples highlight SA’s value in capturing nuanced sentiments and informing pedagogical decisions. The chapter concludes by discussing emerging trends such as multimodal analysis, real-time feedback, and explainable AI, emphasizing the importance of ethical practices.