Ebru KARAAHMETOĞLU, Süleyman ERSÖZ, Ahmet Kürşad TÜRKER, Volkan ATEŞ, Ali Firat İNAL

Makine Öğrenmesi Yöntemleri İle Günümüz Ve Geleceğe Yönelik Meslek Tahminlerinin Değerlendirilmesi : Türkiye'den Ampirik Deliller

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, Türkiye'deki kurumlara ait belgeler de dahil olmak üzere, çeşitli Türkçe verilere denetimli öğrenme algoritmaları ve kümeleme yöntemleri uygulanarak, Türkiye'deki 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, Türkiye'deki 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.

Anahtar Kelimeler:

metin madenciliği, makine öğrenmesi, meslekler, denetimli öğrenme

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

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 Turkey's 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.

Keywords:

text mining, machine learning, professions, supervised learning,

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