Call centers play a key role in the management of customer relationships in the modern business world. However, the growing demand for their services presents significant challenges, particularly in terms of staffing and handling increasing call volumes. This paper addresses these issues by presenting an AI-driven text classification framework tailored for the Republic of Turkiye Ministry of Trade Call Centre (MTCC), with the aim of automatically routing calls to relevant departments. Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. Through a comprehensive analysis, the study answers key research questions regarding optimal classifiers and vectorization methods. The proposed solution not only improves the efficiency of MTCC's call routing but also provides researchers with practical insights regarding Turkish text classification. The results indicate that a combination of the Random Forest classifier and Word2Vec text vectorization method is the optimal model that can manage to route calls in real-time.
We would like to thank the General Directorate of Information Technologies of the Ministry of Trade in Turkiye for generously providing access to the call center data for the purposes of this study.
Primary Language | English |
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Subjects | Computer Software |
Journal Section | Articles |
Authors | |
Early Pub Date | April 27, 2024 |
Publication Date | April 30, 2024 |
Submission Date | December 9, 2023 |
Acceptance Date | March 14, 2024 |
Published in Issue | Year 2024Volume: 7 Issue: 1 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License