This Chapter examines emotion theories from the perspectives of psychology, sociology, neuroscience, and biology, emphasising the role of subjective experiences such as affect, feelings, sentiment, and opinions. It highlights various models of emotion classification, specifically distinguishing between categorical and dimensional approaches. Categorical models of emotion classify emotions into distinct categories: happiness, sadness, anger, fear, and surprise. These models help distinguish between clear emotional states, facilitating their application in emotion recognition technologies. Dimensional models, such as the circumplex model of affect, depict emotions along continuous scales and represent emotions on continuous scales, such as arousal and valence. The dimensional models emphasise the intensity and complexity of emotional experiences, which can help understand the nuances and subtleties of emotional responses. Additionally, Scherer’s Component Process Model (CPM) is introduced. This model integrates dimensional and categorical views by describing emotions through a series of evaluative cognitive processes, each potentially varying in intensity. This multidimensional framework enhances our understanding of the complex spectrum and potential combinations of human emotions. The chapter concludes by proposing a framework that integrates these theoretical concepts with practical Natural Language Processing (NLP) techniques to detect emotions in text, bridging theoretical foundations with computational applications.