Understanding human emotional behavior is a complex but essential task when aiming to offer a better user experience through the incorporation of Affective Computing techniques. The integration of these techniques can lead to more intuitive and emotionally intelligent interactions between users and systems. In a society characterized by ethnic and cultural diversity, it is also necessary to understand how different individuals react to a given stimulus so that adaptations and interventions in the software can be effective. In this context, this study discusses two approaches to comparing emotional responses of different individuals to the same emotional stimulus. By leveraging advanced data analysis and machine learning methods, the research aims to provide deeper insights into emotional patterns. In addition to highlighting the importance of discussing the characteristics and particularities of each approach, the study presents a validation of these approaches, identifying similarities - and distinctions - in the emotional responses of 39 individuals. The results not only demonstrate the effectiveness of the approaches but also suggest their complementarity.