The intuition that some people are better at understanding and regulating their emotions, and lead richer emotional lives has been variously defined and operationalized in the emotion literature. Constructs such as emotional awareness, clarity, diversity, range, complexity, and granularity have been introduced as ways of describing and measuring the quality of emotional life, and many have been linked to positive mental and physical health outcomes. However, the relationship between these constructs is unclear, hindering the ability to meaningfully compare findings and (ultimately) identify critical paths for intervention. Additionally, the measures used to capture these constructs are often global, retrospective reports that provide static summaries biased by beliefs about the self. The construct of emotional granularity is an exception in that it examines the patterns of emotion endorsements in aggregated experience sampling data, yet this measure still fails to capture individual variation over time. We address these problems with a twofold approach. First, we use a construct meta-analytic procedure to assess the theoretical basis for quality of emotional life. Next, we introduce network analysis as a methodological framework for measuring emotional granularity dynamically and over time. We further explore how network analysis can be used to describe individual differences in the semantic space for emotion concepts, and how network metrics (e.g., number of nodes) may be used to more precisely operationalize other constructs for quality of emotional life (e.g., emotional diversity).