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
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 presenti...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2024-04-01
|
| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/3588710 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849724171991384064 |
|---|---|
| author | Muammer Özdemir Yasin Ortakcı |
| author_facet | Muammer Özdemir Yasin Ortakcı |
| author_sort | Muammer Özdemir |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b53f858867194b219e3b231f1898e50f |
| institution | DOAJ |
| issn | 2636-8129 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-b53f858867194b219e3b231f1898e50f2025-08-20T03:10:49ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292024-04-0171466010.35377/saucis...140241428An 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 CenterMuammer Özdemirhttps://orcid.org/0000-0002-3866-7041Yasin Ortakcı0https://orcid.org/0000-0002-0683-2049KARABÜK ÜNİVERSİTESİCall 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.https://dergipark.org.tr/en/download/article-file/3588710text classificationword2vecglovetf-idfcall centers |
| spellingShingle | Muammer Özdemir Yasin Ortakcı 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 Sakarya University Journal of Computer and Information Sciences text classification word2vec glove tf-idf call centers |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | text classification word2vec glove tf-idf call centers |
| url | https://dergipark.org.tr/en/download/article-file/3588710 |
| work_keys_str_mv | AT muammerozdemir ananalysisofintelligentturkishtextclassificationmodelsforroutingcallsincallcentersacasestudyontherepublicofturkiyeministryoftradecallcenter AT yasinortakcı ananalysisofintelligentturkishtextclassificationmodelsforroutingcallsincallcentersacasestudyontherepublicofturkiyeministryoftradecallcenter AT muammerozdemir analysisofintelligentturkishtextclassificationmodelsforroutingcallsincallcentersacasestudyontherepublicofturkiyeministryoftradecallcenter AT yasinortakcı analysisofintelligentturkishtextclassificationmodelsforroutingcallsincallcentersacasestudyontherepublicofturkiyeministryoftradecallcenter |