Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 06 Sayı: 2, 46 - 52, 31.12.2022
https://doi.org/10.34110/forecasting.1173063

Öz

Kaynakça

  • [1] Tıp Veri Kümesi için Gizli Dirichlet Ayrımı Latent Dirichlet Allocation for Medical Dataset Ekin Ekinci , Sevinç İlhan Omurca , Elif Kırık , Şeymanur Taşçı 1 DEU FMD 22(64), 67-80, 2020 68.
  • [2] https://medium.com/@yildizhangocmen/nlp-konu-modelleme-topic-modelling-2852f28bceca
  • [3] Agrawal, A., Fu, W., Menzies, T. 2018. What is wrong with topic modelling? And how tofix it using search based software engineering, Information and Software Technology, Cilt. 98, s. 74-88. DOI: 10.1016/j.infsof.2018.02.005.
  • [4] Blei, D. M., Ng, A. Y. 2003. Latent dirichlet allocation. the Journal of machine Learning research , 3, 993-1022.
  • [5] https://medium.com/@anilguven1055/latent-dirichlet-allocation-lda-algoritmas%C4%B1-13154d246e05.

Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction

Yıl 2022, Cilt: 06 Sayı: 2, 46 - 52, 31.12.2022
https://doi.org/10.34110/forecasting.1173063

Öz

In this study, the performance of the proposed sample selection method was evaluated on some basic classifiers by conducting a basic literature review on the use of topic modelling methods by considering the online evaluations of the employees in order to determine and analyse the job satisfaction factors. In addition, the effectiveness of different representation structures is evaluated in order to represent the data sets effectively and the main results are obtained regarding the use of classification ensemble methods in the field of text mining. In this work it was emphasized that machine learning methods can achieve high performance in classification and work effectively and scalable with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple, and Microsoft were evaluated. Within the scope of this study, a text mining and artificial intelligence-based method will be developed, and a solution will be brought to text mining with artificial intelligence methods.

Kaynakça

  • [1] Tıp Veri Kümesi için Gizli Dirichlet Ayrımı Latent Dirichlet Allocation for Medical Dataset Ekin Ekinci , Sevinç İlhan Omurca , Elif Kırık , Şeymanur Taşçı 1 DEU FMD 22(64), 67-80, 2020 68.
  • [2] https://medium.com/@yildizhangocmen/nlp-konu-modelleme-topic-modelling-2852f28bceca
  • [3] Agrawal, A., Fu, W., Menzies, T. 2018. What is wrong with topic modelling? And how tofix it using search based software engineering, Information and Software Technology, Cilt. 98, s. 74-88. DOI: 10.1016/j.infsof.2018.02.005.
  • [4] Blei, D. M., Ng, A. Y. 2003. Latent dirichlet allocation. the Journal of machine Learning research , 3, 993-1022.
  • [5] https://medium.com/@anilguven1055/latent-dirichlet-allocation-lda-algoritmas%C4%B1-13154d246e05.
Toplam 5 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Matematik
Bölüm Articles
Yazarlar

Ali Özdemir 0000-0001-9330-7084

Aytuğ Onan 0000-0002-9434-5880

Vildan Çınarlı Ergene 0000-0002-1220-3337

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 9 Eylül 2022
Kabul Tarihi 12 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 06 Sayı: 2

Kaynak Göster

APA Özdemir, A., Onan, A., & Çınarlı Ergene, V. (2022). Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction. Turkish Journal of Forecasting, 06(2), 46-52. https://doi.org/10.34110/forecasting.1173063
AMA Özdemir A, Onan A, Çınarlı Ergene V. Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction. TJF. Aralık 2022;06(2):46-52. doi:10.34110/forecasting.1173063
Chicago Özdemir, Ali, Aytuğ Onan, ve Vildan Çınarlı Ergene. “Topic Modelling and Artificial Intelligence Based Method Using Online Employee Assessments to Analyse Job Satisfaction”. Turkish Journal of Forecasting 06, sy. 2 (Aralık 2022): 46-52. https://doi.org/10.34110/forecasting.1173063.
EndNote Özdemir A, Onan A, Çınarlı Ergene V (01 Aralık 2022) Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction. Turkish Journal of Forecasting 06 2 46–52.
IEEE A. Özdemir, A. Onan, ve V. Çınarlı Ergene, “Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction”, TJF, c. 06, sy. 2, ss. 46–52, 2022, doi: 10.34110/forecasting.1173063.
ISNAD Özdemir, Ali vd. “Topic Modelling and Artificial Intelligence Based Method Using Online Employee Assessments to Analyse Job Satisfaction”. Turkish Journal of Forecasting 06/2 (Aralık 2022), 46-52. https://doi.org/10.34110/forecasting.1173063.
JAMA Özdemir A, Onan A, Çınarlı Ergene V. Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction. TJF. 2022;06:46–52.
MLA Özdemir, Ali vd. “Topic Modelling and Artificial Intelligence Based Method Using Online Employee Assessments to Analyse Job Satisfaction”. Turkish Journal of Forecasting, c. 06, sy. 2, 2022, ss. 46-52, doi:10.34110/forecasting.1173063.
Vancouver Özdemir A, Onan A, Çınarlı Ergene V. Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction. TJF. 2022;06(2):46-52.

INDEXING

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