Öz
Intense competition in the automotive industry necessitates the continuous improvement culture. Employee suggestions and suggestion systems are important components of this culture. The fact that content of the suggestion systems consists of texts has made them suitable data sets for advanced text mining studies. Analyzing employee suggestions with topic modeling will make it possible to assess which subjects are most often received, which subjects should be focused on, and to make predictions about the future improvements. In this study, Latent Dirichlet Allocation (LDA), one of the topic modeling methods, was used for the analysis of the employee suggestions of an automotive company. The most common type of suggestion is “positive suggestions with no return”. These recommendations are generally related to occupational health and safety. The second most frequently given suggestions are "suggestions", those are providing profit to the company. In the 3rd rank, there are “fast kaizen from suggestion”, which are high-yielding suggestions that can be achieved in a short time. While the fourth rank most common suggestions are "referred to be evaluated", "recommendations that will not be put into action" suggestions are the least given type.
Anahtar Kelimeler
employee suggestions latent dirichlet allocation Topic modeling
Kaynakça
- Agrawal, A., Fu, W. ve Menzies, T. (2018). What is wrong with topic modeling and how to fix it using search-based software engineering. Information and Software Technology, 98, 74-88.
- Alrumayyan N., Bawazeer S., AlJurayyad R. ve Al-Razgan M. (2018). Analyzing User Behaviors: A Study of Tips in Foursquare. In: Alenezi M., Qureshi B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham.
- Altıntaş, V., Albayrak, M. ve Topal, K. (2021). Kanser hastalığı paylaşımları için Dirichlet ayrımı ile gizli konu modelleme. Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 2183-2196. Balasubramanian S., Kaitheri S., Nanath K., Sreejith S. ve Paris C.M. (2021). Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling. In: Wörndl W., Koo C., Stienmetz J.L. (eds) Information and Communication Technologies in Tourism 2021. Springer, Cham.
- Blei, D.M., Ng, A.Y. ve Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
- Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y. ve Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl, 78, 15169–15211.
- Karkhanis, G.V., Chandnani, S.U., ve Chakraborti, S. (2022). Analysis of employee perception of employer brand: A comparative study across business cycles using structural topic modelling. Journal of Business Analytics, 1-17.
- Khaleq A.A. ve Ra I. (2019). Twitter Analytics for Disaster Relevance and Disaster Phase Discovery. In: Arai K., Bhatia R., Kapoor S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham.
- Kherwa, P. ve Bansal, P. (2018). Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems, 159623.
- Onan, A., Yalçın, A. ve Atik, E. (2020). Üniversite bilgi yönetim sistemi servis destek taleplerinin konu modelleme tabanlı analizi. Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, 389-397.
- Pröllochs, N. ve Feuerriegel, S. (2020). Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57, 103070.
- Rhoden, I., Ball, C.S., Vögele, S. Ve Kuckshinrichs, W. (2022). Minding the gap-relating disclosure to contexts of sustainability reporting in the automotive industry. Corporate Social Responsibility and Environmental Management, 1-12.
- Schimiedel, T., Müller, O. ve Brocke, J.V. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22 (4), 941-968.
- Symitsi, E., Stamolampros, P., Daskalakis, G. ve Korfiatis, N. (2021). The informational value of employee online reviews. European Journal of Operational Research, 288, 605–619.
- Vayansky, I. ve Kumar, S.A.P. (2020). A review of topic modeling methods. Information Systems, 94, 101582.
- Wang, W., Dinh, J., Jones, K.S., Upadhyay, S. ve Yang, J. (2022). Machine learning text analysis of corporate diversity statements predicts employees’ online ratings. Academy of Management Proceedings, 15107.
Öz
Otomotiv endüstrisindeki yoğun rekabet, sürekli iyileştirme kültürünü zorunlu hale getirmektedir. Çalışan önerileri ve öneri sistemleri bu kültürün önemli bileşenleridir. Öneri sistemlerinin içeriğinin metinlerden oluşması, onları ileri metin madenciliği çalışmaları için uygun veri setleri haline getirmiştir. Çalışan önerilerinin konu modelleme ile analiz edilmesi, en çok hangi konularda öneriler geldiğini, hangi konulara yoğunlaşılması gerektiğini ve gelecekteki iyileştirmelerle ilgili tahminler yapabilmeyi olanaklı hale getirebilecektir. Bu çalışmada, bir otomotiv firmasının çalışan önerilerinin analizi için, konu modellemeye ait yöntemlerden, “Gizli Dirichlet Ayrımı (GDA)” kullanılmıştır. En çok verilen öneri çeşidi, “getirisi olmayan olumlu” önerilerdir. Bu öneriler, genellikle iş sağlığı ve güvenliği ile ilgilidir. İkinci sıradaki en çok verilen öneriler ise “öneri”, firmaya kazanç sağlayan önerilerdir. Üçüncü sırada, “öneriden hızlı kaizene” yani kısa sürede sonuç alınabilen, getirisi yüksek öneriler bulunmaktadır. Dördüncü sırada, “değerlendirilmek üzere havale” edilen öneriler bulunurken, en az verilen öneri türünün ise “devreye alınmayacak öneriler” olduğu değerlendirilmiştir.
Anahtar Kelimeler
Kaynakça
- Agrawal, A., Fu, W. ve Menzies, T. (2018). What is wrong with topic modeling and how to fix it using search-based software engineering. Information and Software Technology, 98, 74-88.
- Alrumayyan N., Bawazeer S., AlJurayyad R. ve Al-Razgan M. (2018). Analyzing User Behaviors: A Study of Tips in Foursquare. In: Alenezi M., Qureshi B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham.
- Altıntaş, V., Albayrak, M. ve Topal, K. (2021). Kanser hastalığı paylaşımları için Dirichlet ayrımı ile gizli konu modelleme. Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 2183-2196. Balasubramanian S., Kaitheri S., Nanath K., Sreejith S. ve Paris C.M. (2021). Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling. In: Wörndl W., Koo C., Stienmetz J.L. (eds) Information and Communication Technologies in Tourism 2021. Springer, Cham.
- Blei, D.M., Ng, A.Y. ve Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
- Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y. ve Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl, 78, 15169–15211.
- Karkhanis, G.V., Chandnani, S.U., ve Chakraborti, S. (2022). Analysis of employee perception of employer brand: A comparative study across business cycles using structural topic modelling. Journal of Business Analytics, 1-17.
- Khaleq A.A. ve Ra I. (2019). Twitter Analytics for Disaster Relevance and Disaster Phase Discovery. In: Arai K., Bhatia R., Kapoor S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham.
- Kherwa, P. ve Bansal, P. (2018). Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems, 159623.
- Onan, A., Yalçın, A. ve Atik, E. (2020). Üniversite bilgi yönetim sistemi servis destek taleplerinin konu modelleme tabanlı analizi. Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, 389-397.
- Pröllochs, N. ve Feuerriegel, S. (2020). Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57, 103070.
- Rhoden, I., Ball, C.S., Vögele, S. Ve Kuckshinrichs, W. (2022). Minding the gap-relating disclosure to contexts of sustainability reporting in the automotive industry. Corporate Social Responsibility and Environmental Management, 1-12.
- Schimiedel, T., Müller, O. ve Brocke, J.V. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22 (4), 941-968.
- Symitsi, E., Stamolampros, P., Daskalakis, G. ve Korfiatis, N. (2021). The informational value of employee online reviews. European Journal of Operational Research, 288, 605–619.
- Vayansky, I. ve Kumar, S.A.P. (2020). A review of topic modeling methods. Information Systems, 94, 101582.
- Wang, W., Dinh, J., Jones, K.S., Upadhyay, S. ve Yang, J. (2022). Machine learning text analysis of corporate diversity statements predicts employees’ online ratings. Academy of Management Proceedings, 15107.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 23 Haziran 2023 |
| Yayımlanma Tarihi | 30 Haziran 2023 |
| Gönderilme Tarihi | 21 Mayıs 2022 |
| Kabul Tarihi | 16 Kasım 2022 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 9 Sayı: 1 |
