Improving Customer Service with Automatic Topic Detection in User Emails

This study introduces a novel natural language processing pipeline that enhances customer service efficiency at Telekom Srbija, a leading Serbian telecommunications company, through automated email topic detection and labeling. Central to the pipeline is BERTopic, a modular framework that allows unsupervised topic modeling. After a series of preprocessing and postprocessing steps, we assign one of 12 topics and several additional labels to incoming emails, allowing the customer service to filter and access them through a custom-made application. The model's performance was evaluated by assessing the speed and correctness of the automatically assigned topics, with a weighted average processing time of 0.041 seconds per email and a weighted average F1 score of 0.96. The pipeline shows broad applicability across languages, particularly to those that are low-resourced and morphologically rich. The system now operates in the company's production environment, streamlining customer service operations through automated email classification.

@article{bašaragin2025_2502.19115,
  title={ Improving Customer Service with Automatic Topic Detection in User Emails },
  author={ Bojana Bašaragin and Darija Medvecki and Gorana Gojić and Milena Oparnica and Dragiša Mišković },
  journal={arXiv preprint arXiv:2502.19115},
  year={ 2025 }
}