Improving Automated Categorization of Customer Requests with Recent Advances in Natural Language Processing

Filip Koukal, František Dařena, Roman Ježdík and Jan Přichystal
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Filip Koukal: Mendel University in Brno, Czech Republic
Roman Ježdík: ALVAO, s. r. o., Žďár nad Sázavou, Czech Republic
Jan Přichystal: Mendel University in Brno, Czech Republic

European Journal of Business Science and Technology, 2024, vol. 10, issue 2, 173-184

Abstract: In this paper, we focus on the categorization of tickets in service desk systems. We employ modern neural network-based artificial intelligence methods to improve the performance of current systems and address typical problems in the domain. Special attention is paid to balancing the ticket categories, selecting a suitable representation of text data, and choosing a classification model. Based on experiments with two real-world datasets, we conclude that text preprocessing, balancing the ticket categories, and using the representations of texts based on fine-tuned transformers are crucial for building successful classifiers in this domain. Although we could not directly compare our work to other research the results demonstrate superior performance to similar works.

Keywords: service desk systems; customer requests classification; transformer models; machine learning (search for similar items in EconPapers)
JEL-codes: C89 L86 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:men:journl:v:10:y:2024:i:2:p:173-184

DOI: 10.11118/ejobsat.2024.010

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