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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2026 / Cilt: 9 Sayı: 2026

A Machine Learning-Based Automatic E-mail Classification System for Corporate Complaint Management

Kurumsal Şikayet Yönetimi için Makine Öğrenmesi Tabanlı Otomatik E-posta Sınıflandırma Sistemi

İsmail Enes Parlak(BURSA TEKNİK ÜNİVERSİTESİ)0000-0002-0694-9220
Pages
1–10
Publication Date
2026-03-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings revealed that the developed system achieved an average F1-score of 87.75%.

📌 3 Key Takeaways

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Objective & Hypothesis:Manual management of complaint emails following corporate gifting processes, specifically the distribution of employee benefits such as Ramadan or New Year packages, creates a significant operational burden for procurement departments.
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Key Findings & Data:In the study, to overcome privacy constraints associated with real-world corporate data, a unique dataset containing 1,870 synthetic complaint emails was generated using large language models based on actual operational scenarios.
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Conclusion & Impact:The findings revealed that the developed system achieved an average F1-score of 87.75%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kurumsal Şikayet Yönetimi · Çok Etiketli Sınıflandırma · Açıklanabilir Yapay Zekâ

Abstract

Manual management of complaint emails following corporate gifting processes, specifically the distribution of employee benefits such as Ramadan or New Year packages, creates a significant operational burden for procurement departments. This study focuses on this specific problem and aims to develop an artificial intelligence (AI)-supported, multi-label system for the automatic classification and management of complaint emails. The system classifies complaints into four main categories: product type, package type, complaint type, and responsible person. In the study, to overcome privacy constraints associated with real-world corporate data, a unique dataset containing 1,870 synthetic complaint emails was generated using large language models based on actual operational scenarios. Texts were vectorized using the TF-IDF method, and the performance of ten machine learning algorithms, including CatBoost, XGBoost, and Support Vector Machines (SVM), was evaluated. The findings revealed that the developed system achieved an average F1-score of 87.75%. Distinguishing itself from the extensive literature primarily focused on spam and phishing detection, this study offers a unique contribution by applying the SHAP (SHapley Additive exPlanations) method to the domain-specific problem of multi-label corporate complaint management. In this way, trust in the system is increased by explaining which words the model predictions are based on. The developed explainable AI-supported system offers an effective and interpretable solution for increasing automation and efficiency in complaint management processes.

Özet

Kurumsal hediyeleşme süreçleri sonrasında, özellikle çalışanlara yan hak olarak sunulan Ramazan, yılbaşı gibi hediye paketlerinin dağıtımının ardından gelen şikayet e-postalarının manuel yönetimi, satın alma departmanları için ciddi bir operasyonel yük oluşturmaktadır. Bu çalışma, bu spesifik probleme odaklanarak, şikayet e-postalarının otomatik sınıflandırılması ve yönetimi için yapay zekâ (YZ) destekli, çok etiketli bir sistem geliştirmeyi amaçlamaktadır. Sistem, şikayetleri; ürün türü, paket türü, şikâyet türü ve sorumlu kişi olmak üzere dört ana kategoride sınıflandırmaktadır. Çalışmada, gerçek şirket verilerinin gizlilik kısıtları nedeniyle, büyük dil modelleri kullanılarak ve gerçek operasyonel senaryolar temel alınarak 1.870 adet sentetik şikâyet e-postası içeren özgün bir veri seti oluşturulmuş ve TF-IDF yöntemiyle metinler vektörleştirilmiştir. CatBoost, XGBoost ve Destek Vektör Makineleri (SVM) dâhil olmak üzere on farklı makine öğrenmesi algoritmasının performansı karşılaştırmalı olarak değerlendirilmiştir. Elde edilen bulgular, geliştirilen sistemin %87.75 ortalama F1-skoru başarısı gösterdiğini ortaya koymuştur. Literatürdeki spam ve oltalama odaklı çalışmaların aksine, bu çalışmanın özgün katkısı; çok etiketli kurumsal şikayet yönetimi problemine SHAP (SHapley Additive exPlanations) yöntemini uygulayarak model kararlarını şeffaf hale getirmesidir. Bu sayede, model tahminlerinin hangi kelimelere dayandığı açıklanarak sisteme duyulan güven artırılmıştır. Geliştirilen bu açıklanabilir YZ destekli sistem, kurumsal hediye paketleriyle ilgili şikayet yönetimi süreçlerinde otomasyon ve verimlilik artışı için etkin ve yorumlanabilir bir çözüm sunmaktadır.

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Cite This Article

Parlak, İ. E. (2026). A Machine Learning-Based Automatic E-mail Classification System for Corporate Complaint Management. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 9 Sayı: 2026, 1–10. https://doi.org/10.38016/jista.1806772

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