Performing DISC Personal Inventory Analysis in Job Postings Using Artificial Intelligence Methods

Alperen Sayar; Ahmet Yıldız; Tuna Çakar; Dilara Şengünoğlu; Seyit Ertuğrul

Performing DISC Personal Inventory Analysis in Job Postings Using Artificial Intelligence Methods

Abstract

One of the application fields of DISC self-evaluation analysis was introduced to predict people's performance and orientation in their working life. Each letter in the word DISC represents an essential personal characteristic, dividing the profiles of people in business life into four essential parts. In the current study, DISC analysis is conducted on job postings to match the person with the job posting. The current study was based on the analysis of 3 different datasets with job postings in English, Turkish and Romanian prepared by using web scraping methods and then labeled in accordance with DISC criteria. Several different machine learning algorithms have been performed on the DISC analysis outputs, and they reached the best results with accuracy values of around over 96% on the English dataset, around over 95% on the Turkish dataset, and around over 96% on the Romanian dataset, for both D, I, S, C models.

Keywords

DISC , self-evaluation , job postings , XGBoost , LSTM

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Öğrenmesi Algoritmaları

Bölüm

Araştırma Makalesi

Yazarlar

Alperen Sayar ^*
Türkiye

Ahmet Yıldız Bu kişi benim
Türkiye

Tuna Çakar Bu kişi benim
Türkiye

Dilara Şengünoğlu Bu kişi benim
Türkiye

Seyit Ertuğrul Bu kişi benim
Türkiye

Yayımlanma Tarihi

21 Kasım 2024

Gönderilme Tarihi

29 Eylül 2022

Kabul Tarihi

31 Aralık 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 6 Sayı: 2

IZ

https://izlik.org/JA28WT68LW

IEEE

[1]A. Sayar, A. Yıldız, T. Çakar, D. Şengünoğlu, ve S. Ertuğrul, “Performing DISC Personal Inventory Analysis in Job Postings Using Artificial Intelligence Methods”, DataSCI, c. 6, sy 2, ss. 5–12, Kas. 2024, [çevrimiçi]. Erişim adresi: https://izlik.org/JA28WT68LW