Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey

Ebru Karaahmetoğlu; Süleyman Ersöz; Ahmet Kürşad Türker; Volkan Ateş; Ali Firat İnal

Makale detayları

Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey

Yazarlar:

Ebru Karaahmetoğlu | Süleyman Ersöz | Ahmet Kürşad Türker | Volkan Ateş | Ali Firat İnal

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Politeknik Dergisi Vol.26 Issue 1, pp.107-124

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285726

pISSN:

2147-9429

Anahtar kelimeler:

Özet:

 For the purpose of evaluating present and future trends of professions within the labor market, text mining approach could be an alternative to more traditional approaches such as employer surveys. Specifically, machine learning algorithms are used for making accurate predictions about the future directions of the professions which consequently will influence professional development of labour force. The aim of this study is to investigate the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkeys institutions. In this study, the popular professions were predicted with an accuracy rate between 0.81 and 0.93 thorough various machine learning algorithms. It was discovered that methodologically perceptron and stochastic gradient descent algorithms demonstrated superiority over other algorithms thanks to their intelligence functions. Furthermore, the analysis of current professions in Turkey revealed that the class of Professional occupations, Managers and Technicians and assistant professional members were popular, and according to the analysis of the future, information technology-based occupations will be important. Although limited Turkish data sources for the analysis of future, results with an accuracy of nearly 1 were produced.

 For the purpose of evaluating present and future trends of professions within the labor market, text mining approach could be an alternative to more traditional approaches such as employer surveys. Specifically, machine learning algorithms are used for making accurate predictions about the future directions of the professions which consequently will influence professional development of labour force. The aim of this study is to investigate the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkeys institutions. In this study, the popular professions were predicted with an accuracy rate between 0.81 and 0.93 thorough various machine learning algorithms. It was discovered that methodologically perceptron and stochastic gradient descent algorithms demonstrated superiority over other algorithms thanks to their intelligence functions. Furthermore, the analysis of current professions in Turkey revealed that the class of Professional occupations, Managers and Technicians and assistant professional members were popular, and according to the analysis of the future, information technology-based occupations will be important. Although limited Turkish data sources for the analysis of future, results with an accuracy of nearly 1 were produced.

 For the purpose of evaluating present and future trends of professions within the labor market, text mining approach could be an alternative to more traditional approaches such as employer surveys. Specifically, machine learning algorithms are used for making accurate predictions about the future directions of the professions which consequently will influence professional development of labour force. The aim of this study is to investigate the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkeys institutions. In this study, the popular professions were predicted with an accuracy rate between 0.81 and 0.93 thorough various machine learning algorithms. It was discovered that methodologically perceptron and stochastic gradient descent algorithms demonstrated superiority over other algorithms thanks to their intelligence functions. Furthermore, the analysis of current professions in Turkey revealed that the class of Professional occupations, Managers and Technicians and assistant professional members were popular, and according to the analysis of the future, information technology-based occupations will be important. Although limited Turkish data sources for the analysis of future, results with an accuracy of nearly 1 were produced.

 İşgücü piyasasındaki mesleklerin mevcut ve gelecekteki eğilimlerini belirlemede metin madenciliği yaklaşımı, işveren anketleri gibi geleneksel yöntemlere alternatif olarak kullanılabilir. Teknik olarak, iş gücünün mesleki gelişimini etkileyecek mesleklerin, gelecekteki eğilimleri hakkında doğru tahminlerde bulunmak için makine öğrenme algoritmaları kullanılmaktadır. Bu çalışmanın amacı, Türkiyedeki kurumlara ait belgeler de dahil olmak üzere, çeşitli Türkçe verilere denetimli öğrenme algoritmaları ve kümeleme yöntemleri uygulanarak, Türkiyedeki geleceğin ve şimdiki mesleklerin araştırılmasıdır. Çalışmada, çeşitli makine öğrenme algoritmaları aracılığıyla 0.81 ve 0.93 arasında bir doğruluk oranıyla, popüler meslekler tahmin edilmiştir. Metodolojik olarak Perceptron ve Stokastik Gradyan İniş algoritmalarının, içerdiği zeka fonksiyonları sayesinde diğer algoritmalara göre üstünlük gösterdiği keşfedilmiştir. Ayrıca, Türkiyedeki mevcut mesleklerin analizi, Profesyonel meslekler, Yönetici ve Teknisyen ve meslek mensubu yardımcıları sınıfının popüler olduğu ve gelecek analizine göre bilgi teknolojisi tabanlı mesleklerin önemli olacağı çıkarımı yapılmıştır. Geleceğin analizi için sınırlı Türkçe veri kaynakları olmasına rağmen, yaklaşık 1 doğrulukta sonuçlar üretilmiştir.

Makale detayları

Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey

Yazarlar:

Ebru Karaahmetoğlu | Süleyman Ersöz | Ahmet Kürşad Türker | Volkan Ateş | Ali Firat İnal

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Politeknik Dergisi Vol.26 Issue 1, pp.107-124

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285726

pISSN:

2147-9429

text mining

machine learning

professions

supervised learning

metin madenciliği

makine öğrenmesi

meslekler

denetimli öğrenme

Sayı

Makale detayları

Yazarlar: Ebru Karaahmetoğlu | Süleyman Ersöz | Ahmet Kürşad Türker | Volkan Ateş | Ali Firat İnal

Yayıncı: Gazi University

Ana Başlıklar: Mühendislik

Dergi: Politeknik Dergisi Vol.26 Issue 1, pp.107-124

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001285726

pISSN: 2147-9429

Sayı

Yazarlar:

Ebru Karaahmetoğlu | Süleyman Ersöz | Ahmet Kürşad Türker | Volkan Ateş | Ali Firat İnal

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Politeknik Dergisi Vol.26 Issue 1, pp.107-124

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285726

pISSN:

2147-9429