TEXT CLASSIFICATION WITH FREQUENCY-BASED TEXT VECTORISATION METHODS FOR ENHANCING CALL CENTRE EFFICIENCY

Year 2023, Volume: 1 Issue: 2, 122 - 138, 02.02.2024

Muammer Özdemir , Yasin Ortakcı

Abstract

In today's business world, many transactions take place over the phone or online. Call centers play a significant role in dealing with different situations and solving problems that come with the large volume of global business. As an interface between companies/institutions and customers, call centers aim to eliminate problems, correct mistakes, resolve conflicts, and increase customer satisfaction. The traditional approach involves customer service agents handling inquiries and complaints, but human error can hinder effective problem resolution. Intelligent assistant applications have emerged to augment the skills of customer service agents, improve performance, and maximize customer satisfaction. This study focuses on addressing the challenges faced by the Republic of Turkiye Ministry of Trade Call Center in the (RTMTCC), which handles over 10,000 calls per day. For this purpose, it introduces an intelligent framework that uses AI-driven methods and frequency-based text vectorization techniques to efficiently route calls to relevant departments, with the aim of increasing customer satisfaction and reducing economic losses. Using historical call texts, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF), the study evaluates the performance of five different classifiers: Stochastic Gradient Descent (SGD), Logistic Regression (LR), Naive Bayes (NB), Adaptive Boosting (AdaBoost), Artificial Neural Networks (ANN). The results indicate that the AdaBoost classifier generally outperforms others in both text vectorization approaches by reaching higher precision, recall and f1-score values. The study provides new approaches to automate call routing, evaluates how to classify text effectively, and shows the strengths and weaknesses of different text analysis methods, helping us to understand call center operations better.

Keywords

Text classification, Text vectorization, TF-IDF, BoW, Call centers

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