What Makes Us Feel Good? A Data-Driven Investigation of Positive Emotion Experience

What does it mean to feel good? Is our experience of gazing in awe at a majestic mountain fundamentally different than erupting with triumph when our favorite team wins the championship? Here, we use a semantic space approach to test which positive emotional experiences are distinct from each other based on in-depth personal narratives of experiences involving 22 positive emotions (n = 165; 3,592 emotional events). A bottom-up computational analysis was applied to the transcribed text, with unsupervised clustering employed to maximize internal granular consistency (i.e., the clusters being maximally different and maximally internally homogeneous). The analysis yielded four emotions that map onto distinct clusters of subjective experiences: amusement, interest, lust, and tenderness. The application of the semantic space approach to in-depth personal accounts yields a nuanced understanding of positive emotional experiences. Moreover, this analytical method allows for the bottom-up development of emotion taxonomies, showcasing its potential for broader applications in the study of subjective experiences.