This study investigates the sentiment polarity (positive, negative, neutral) and specific emotions (joy, sadness, anger, surprise, trust, anticipation, disgust, and fear) expressed by Generation Z in digital platform comments regarding seven female duets with famous male singer. A dataset of 500 digital comments (250 from YouTube, 125 from Twitter, 125 from Instagram) was collected. The sample was then refined to include comments from 100 individuals (50 men, 50 women) affiliated with a private university in Mexico City, ensuring gender balance. Sentiment polarity was classified using a Bidirectional Encoder Representations from Transformers (BERT) model, with its hyperparameters (learning rate, epochs, batch size) optimized via a Particle Swarm Optimization (PSO) metaheuristic, leading to a 4% accuracy improvement over default settings. Emotion detection was performed concurrently using the NRC Emotion Lexicon, a lexical database mapping terms to eight emotional categories. Results indicate a clear correlation between musical tone and expressed sentiment: melancholic duets elicited predominantly negative sentiments, whereas more energetic collaborations generated a higher proportion of positive comments and the emotion 'joy.' Furthermore, significant differences using $\chi^2$ ($p < 0.01$) were observed in the distribution of 'anger' and 'sadness' between intimate and collaborative duets. These findings offer valuable insights into how Generation Z, segmented by gender, emotionally interprets this singer's musical productions. This research has significant implications for developing targeted music marketing strategies and content production for digitally native audiences.