The Integrated Variable Emotion Theory and Multi-Dimensional Emotion Model

Emotion AI or Affective Computing (AC) contributes in new ways to improve communication between sensitive humans and computers, which are unemotional. Emotion recognition from the text is an evolving area of research in Natural Language Processing. Emotions influence human behaviour to a great extent. Sometimes actions are based on emotions we feel. Many researchers and Psychologists have provided answers to questions such as how we have emotions and what causes us to have these emotions. They have proposed different theories to explain why humans have emotions and suggest computational models to describe how to classify the emotions. In this paper, we discuss a few emotion models and theories of emotion and briefly describe and suggest a new emotion model. This paper introduces the Integrated Variable Emotion Theory, a novel framework for understanding human emotion that synthesizes key insights from several prominent affective models. While each contributing theory—Basic Emotion Theory, the Circumplex Model of Affect, Plutchik's Wheel of Emotions, Schachter-Singer Two-Factor Theory, Appraisal Theory, and Constructed Emotion Theory—offers valuable perspectives, Integrated Variable Emotion Theory proposes a more holistic and dynamic approach. I posit that emotional experience is not solely determined by innate, basic emotions or purely constructed from contextual cues, but rather emerges from a complex interplay of these factors. Integrated Variable Emotion Theory aims to provide a more comprehensive and nuanced understanding of the multifaceted nature of human emotion. This paper also introduces the multi-dimensional emotion model, a novel approach especially designed to design AI models that understand and respond to human emotion, making our day-to-day interactions more interactive and engaging.