A two-step customer complaint management using ensemble learning for the banking industry

Year 2023, Volume: 16 Issue: 1, 45 - 52, 29.06.2023

Muhammed Mehmet Akgümüş Ali Boyacı

https://doi.org/10.54525/tbbmd.1163852

Abstract

In this study; It is aimed to classify all kinds of notifications from customers in the banking sector, then prioritize these classes and give feedback to the customer according to this priority. In this way, it is aimed to produce quick solutions for priority notifications that will ensure customer satisfaction. In the literature review on the classification of data, high accuracy values; It has been observed that it is obtained by Logistic Regression, Long Short Term Memory (LSTM), Multinominal Naive Bayes and Support Vector Machine (SVM) algorithms. For this reason, training and testing processes were carried out using Natural Language Processing (NLP) methods on a real bank data set with these algorithms. With the two-stage approach presented as a new method, it has been achieved to increase the accuracy values above seventy percent by working with a limited number of data sets.

Keywords

Natural Language Processing, Machine Learning, Turkish Text Classification, Customer Satisfaction, Logistic Regression, Support Vector Machine, Long Short Term Memory, Multinominal Naive Bayes

Bankacılık sektörü için topluluk öğrenimini kullanan iki aşamalı bir müşteri şikayet yönetimi

Abstract

Bu çalışmada; bankacılık sektöründe müşterilerden gelen her türlü bildirimlerin sınıflandırılması, sonrasında bu sınıfların önceliklendirilmesi ve bu önceliğe göre müşteriye geri bildirim verilmesi amaçlanmıştır. Bu sayede müşteri memnuniyeti sağlayacak öncelikli bildirimlere hızlı çözüm üretilebilmesi hedeflenmiştir. Verilerin sınıflandırılmasıyla ilgili yapılan literatür taramasında yüksek doğruluk değerlerinin; Lojistik Regresyon, Uzun Kısa Süreli Bellek, Multinominal Naive Bayes ve Destek Vektör Makinesi algoritmaları ile elde edildiği gözlemlenmiştir. Bu sebeple bu algoritmalarla gerçek bir banka veri seti üzerinde Doğal Dil İşleme (Natural Language Processing - NLP) yöntemleri kullanılarak eğitim ve sınama işlemleri gerçekleştirilmiştir. Yeni bir yöntem olarak sunulan iki aşamalı yaklaşımla sınırlı sayıda veri setiyle çalışılarak doğruluk değerlerini yüzde yetmişin üzerine çıkarılması başarılmıştır.

Keywords

Doğal Dil İşleme, Makine Öğrenimi, Türkçe Metin Sınıflandırma, Müşteri Memnuniyeti, Logistic Regresyon, Destek Vektör Makinesi, Uzun Kısa Süreli Hafıza, Multinominal Naive Bayes

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