# Improving Customer Service with Automatic Topic Detection in User Emails

arXiv: https://arxiv.org/abs/2502.19115 · HTML: https://mlsanity.com/paper/2502.19115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings](https://mlsanity.com/paper/2309.09783/) (2023)
- [Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian](https://mlsanity.com/paper/2402.03067/) (2024)
- [Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining](https://mlsanity.com/paper/2404.05428/) (2024)
- [Universal Model in Online Customer Service](https://mlsanity.com/paper/2402.15666/) (2024)
- [Improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing](https://mlsanity.com/paper/2305.08594/) (2023)

## Abstract (arXiv)

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 customer service to filter and access them through a custom-made application. While applied to Serbian, the methodology is conceptually language-agnostic and can be readily adapted to other languages, particularly those that are low-resourced and morphologically rich. The system 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 system now operates in the company's production environment, streamlining customer service operations through automated email classification.