Dynamic Sentiment Analysis on the Emergence of Pre-Trained Generative Model-Based Applications in Indonesia

The emergence of pre-trained generative model–based applications has intensified sentiment dynamics within Indonesia’s multi-platform digital ecosystem, where sentiment intensity and temporal fluctuations occur simultaneously. To overcome these challenges, this study extends IndoBERT by incorporating a time-aware tokenization mechanism within a fine-grained dynamic sentiment analysis framework. This mechanism is designed to explicitly capture the evolution of sentiment over time. Instead of relying on external embeddings or implicit timestamps, temporal information is injected directly into the IndoBERT tokenizer through explicit temporal tokens, enabling end-to-end temporal adaptation during fine-tuning. We utilized a large-scale dataset harvested from various platforms—including TikTok, Twitter (X), YouTube, and forums—alongside AI-generated content from Gemini, ChatGPT, and Copilot. The dataset was annotated into five fine-grained sentiment classes: very positive, positive, neutral, negative, and very negative. The experimental evaluation demonstrates that the proposed time-aware IndoBERT model attains an average accuracy of 96.38%, exceeding the performance of the baseline BERT and RoBERTa models. Furthermore, ablation studies validate that the inclusion of time-aware tokenization yields quantifiable performance gains, proving that explicit temporal encoding refines sentiment sensitivity and offers sharper insights into the shifting public opinion in Indonesia.

@article{Sinaga2025,
  title     = {Dynamic Sentiment Analysis on the Emergence of Pre-Trained Generative Model-Based Applications in Indonesia},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Frans Mikael Sinaga and Jefri Junifer Pangaribuan and Kelvin and Ferawaty and Andree Emmanuel Widjaja},
  doi       = {10.14569/IJACSA.2025.01612111},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612111}
}