An Automatic and Robust System for Identification of Problematic Call Centre Conversations

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.

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An Automatic and Robust System for Identification of Problematic Call Centre Conversations

Cited by 3 publications

References 10 publications

Are They Likely to Complain on Phish or Spam? A Prediction Model

Are They Likely to Complain on Phish or Spam? A Prediction Model

Predicting Customer Behavior in Support Channels Using Machine Learning

An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center

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