Analyzing Generation Z's Sentiment on Working Hours and Mental Health Using IndoBERT: Evidence from TikTok Discussions
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1292Keywords:
BERT-base Multilingual; Generation Z; Mental Health; Sentiment Analysis; Working HoursAbstract
Working hours exceeding recommended limits may increase burnout, psychological stress, and sleep disturbances, particularly among Generation Z, who highly value mental health and work-life balance. Although public opinions on this issue are increasingly expressed on social media, most existing studies rely on conventional machine learning methods or lexicon-based labeling, which are less effective at capturing contextual meaning and linguistic nuance in informal social media text. To address this gap, this study proposes a transformer-based sentiment analysis framework that employs a fine-tuned BERT-based Multilingual model as the primary sentiment classifier, rather than merely as an automatic labeling tool, to analyze Generation Z's sentiment toward working hours and mental health based on TikTok comments. A total of 2,203 comments were collected through web scraping and processed using cleaning, normalization, tokenization, and BERT-based zero-shot sentiment labeling before being used to fine-tune the classification model. The model was evaluated using accuracy, precision, recall, and F1-score. The results indicate that negative sentiment dominated the dataset (41.13%), followed by positive (37.49%) and neutral (21.38%) sentiments. Frequently occurring terms, such as working hours, work, and resigning, suggest that users' concerns mainly relate to long working hours and the intention to leave their jobs. The fine-tuned model achieved excellent classification performance, although a gap between training and validation performance indicates the need for improved generalization. These findings demonstrate that BERT-based sentiment analysis can provide valuable insights for organizations, policymakers, and human resource practitioners in developing more flexible working-hour policies and mental health support programs tailored to Generation Z.
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Copyright (c) 2026 Alfilia Hilda Rahmatika, Bella Okta Sari Miranda, Imam Adiyana, Isyiffah Falujjah Anugrah Putri
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