TY  - JOUR
AU  - Contreras-Masse, Roberto
AU  - Ochoa-Zezzatti, Alberto
AU  - Castellanos, Humberto Garcia
AU  - Contreras-Moheno, Roberto
PY  - 2025
DA  - 2025/11/18
TI  - Optimized Sentiment Analysis with PSO-BERT for Generation Z’s Emotional Response to Popular Songs
JO  - ICCK Transactions on Swarm and Evolutionary Learning
T2  - ICCK Transactions on Swarm and Evolutionary Learning
JF  - ICCK Transactions on Swarm and Evolutionary Learning
VL  - 1
IS  - 2
SP  - 32
EP  - 49
DO  - 10.62762/TSEL.2025.125300
UR  - https://www.icck.org/article/abs/TSEL.2025.125300
KW  - generation Z
KW  - sentiment analysis
KW  - emotion analysis
KW  - BERT (Bidirectional Encoder Representations from Transformers)
KW  - PSO Metaheuristics (Particle Swarm Optimization)
KW  - NRC Emotion Lexicon
AB  - 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.
SN  - 3069-2962
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{ContrerasMasse2025Optimized,
  author = {Roberto Contreras-Masse and Alberto Ochoa-Zezzatti and Humberto Garcia Castellanos and Roberto Contreras-Moheno},
  title = {Optimized Sentiment Analysis with PSO-BERT for Generation Z’s Emotional Response to Popular Songs},
  journal = {ICCK Transactions on Swarm and Evolutionary Learning},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {32-49},
  doi = {10.62762/TSEL.2025.125300},
  url = {https://www.icck.org/article/abs/TSEL.2025.125300},
  abstract = {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.},
  keywords = {generation Z, sentiment analysis, emotion analysis, BERT (Bidirectional Encoder Representations from Transformers), PSO Metaheuristics (Particle Swarm Optimization), NRC Emotion Lexicon},
  issn = {3069-2962},
  publisher = {Institute of Central Computation and Knowledge}
}