Abstract
Online petitions are an important means for citizens to express their concerns and to interact with government entities. Due to the increase in the number of petitions, manually attributing them to the competent unit in public administration creates a bottleneck, leading to response delays, and potentially even errors. To address this problem, a multi-label classifier using fine-tuned BERT model is suggested. The proposed model, trained on a dataset from the Taiwanese Join Platform, performs reasonably well in predicting the governmental departments in charge of petitions, even when trained on an imbalanced and rather small dataset. The obtained model manages to effectively process petitions and predicts responsible departments, achieving F1 score of 0.61 averaged over 12 categories. The proposed approach would potentially improve government responsiveness, optimize resource allocation, and facilitate online petition processing. Future work would focus on improving the model’s generalization capabilities.
Buryakov, Daniil