SALARY PREDICTION FROM JOB DESCRIPTIONS USING ATTENTION-BASED NLP MODELS

The research introduces a dual deep learning system which predicts salary ranges by processing job descriptions through BERT-based contextual embeddings and structured metadata integration. The proposed method utilizes more than 124,000 LinkedIn job postings to merge BERT-based contextual embeddings with structured information about location and industry and experience level and compensation type. The model uses multi-head attention to identify essential salary-related terms in job descriptions which results in better model interpretability and improved prediction accuracy. The model combines semantic embeddings with tabular data to create a multimodal representation which serves as input for supervised learning with an ordinal-aware loss function. The model achieves stable performance in salary classification across three categories through F1-scores between 0.82 and 0.84. The proposed model achieves excellent generalization capabilities for different sectors and job types while providing precise predictions and clear decision-making processes for salary benchmarking and recruitment analytics applications.

1. Niknejad, N., Kianiani, M., Puthiyapurayil, N.P., and Khan, T.A. Analyzing Data Professional Salaries: Exploring Trends and Predictive Insights. Proceedings of the 8th International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE), (2023). https://www.researchgate.net/publication/376445561_Analyzing_Data_Professional_Salaries_Exploring_Trends_and_Predictive_Insights.

2. Thapa, S. Adult Income Prediction Using Various ML Algorithms. SSRN Electronic Journal, 1 (2023). https://papers.ssrn.com/abstract=4325813.

3. Kuanyshbay, K., Serek, A.G., Shoyinbek, A., Sharipov, F., Shoyinbek, A., Meraliyev, A., and Meraliyev, A. Development of an AI-Based Communication Fraud Detection System. Applied Mathematics & Information Sciences, 19 (4), (2025). https://doi.org/10.18576/amis/190419.

4. Zholshiyeva, L., Zhukabayeba, T., Serek, A., Duisenbek, R., Berdieva, M., and Shapay, N. Deep Learning-Based Continuous Sign Language Recognition. Journal of Robotics and Control (JRC), 6 (3), 1106–1118 (2025).

5. Zholshiyeva, L., Zhukabayeva, T., Baumuratova, D., and Serek, A. Design of QazSL Sign Language Recognition System for Physically Impaired Individuals. Journal of Robotics and Control (JRC), 6 (1), 191–201 (2025).

6. Berlikozha, B., Serek, A., Zhukabayeva, T., Zhamanov, A., and Dias, O. Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods. Journal of Robotics and Control (JRC), 6 (1), 426–436 (2025). https://umy.ac.id.

7. Wang, G. Employee Salaries Analysis and Prediction with Machine Learning. Proceedings of the 2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), 373–378 (2022).

8. Kablaoui, R., and Salman, A. Machine Learning Models for Salary Prediction Dataset Using Python. Proceedings of the 2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 143–147 (2022).

9. Satpute, B. S., Yadav, R., and Yadav, P. K. Machine Learning Approach for Prediction of Employee Salary Using Demographic Information with Experience. Proceedings of the 2023 IEEE Global Conference for Advancement in Technology (GCAT), (2023).

10. Safi, F., and Polash, M.M.A. Mining Job Description to Understand the On-Demand Skills and Expertise in Big Data Analytics. Proceedings of the 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), 583–588 (2022).

11. Korytov, P.V., Kholod, I.I., Gribetskiy, Y.Y., and Andreeva, E.A. Analysis of Approaches for Identifying Key Skills in Vacancies. Proceedings of the 27th International Conference on Soft Computing and Measurements (SCM), 242–245 (2024).

12. Alsayed, N., and Awad, W.S. A Framework for Labor Market Analysis Using Machine Learning. Proceedings of the 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD) (2023).

13. Sharma, A., Singhal, S., and Ajudia, D. Intelligent Recruitment System Using NLP. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Machine Vision (AIMV), (2021).

14. Lalitha, B., Kadiyam, S., Kalidindi, R.V., Vemparala, S.M., Yarlagadda, K., and Chekuri, S.V. Applicant Screening System Using NLP. Proceedings of the International Conference on Innovative Data Communication Technologies and Application (ICIDCA), 379–383 (2023).

15. Warusawithana, S.P., Perera, N.N., Weerasinghe, R.L., Hindakaraldeniya, T.M., and Ganegoda, G.U. Layout-Aware Resume Parsing Using NLP and Rule-Based Techniques. Proceedings of the ICITR 2023 – 8th International Conference on Information Technology Research, (2023).

16. Yaphet, T.A., Putra, M.A.W., Avianny, V.C., Edbert, I.S., and Suhartono, D. Fake Job Vacancy Detection Using Ensemble Voting Classifier. Proceedings of the 2nd International Conference on Technology Innovation and Its Applications (ICTIIA), 1–7 (2024). https://ieeexplore.ieee.org/document/10761155/.

17. Sukumar, J.G., Reddy, M.S.R., Sambangi, N., Abhishek, S., and Anjali, T. Enhancing Salary Projections: A Supervised Machine Learning Approach with Flask Deployment. Proceedings of the 5th International Conference on Inventive Research in Computing Applications (ICIRCA), 693–700 (2023). https://www.researchgate.net/publication/373476796.

18. Mittal, S., Monga, C., Bansal, A., and Singla, N. Analyzing Data Scientist Salaries Dataset Through Machine Learning Algorithms Using Tool “Orange”. Proceedings of the 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA), (2023).

19. Joshi, M., Bhosale, S., and Vyawahare, V.A. Using Fractional Derivative in Learning Algorithm for Artificial Neural Network: Application for Salary Prediction. Proceedings of the IEEE Bombay Section Signature Conference (IBSSC), (2022). https://www.researchgate.net/publication/368518620.

20. Thapa, S. Adult Income Prediction Using Various ML Algorithms. SSRN Electronic Journal, (2023). https://papers.ssrn.com/abstract=4325813.

21. Yuan, J. Big Data Analysis in Human Resources Management: Performance Prediction Based on Employee Network. Proceedings of the IEEE 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), 389–395 (2022).

22. Zheng, D. Simulation Research on College Students’ Employment Prediction Model Based on Decision Tree Classification Algorithm. Proceedings of the 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), 194–199 (2023). https://www.researchgate.net/publication/380339281.

23. Deepa, N., et al. Improving Performance Analysis in Classification with Accuracy of Adult Income Salary Using Novel Gated Residual Neural Network Compared with Logistic Regression. Proceedings of the 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–5 (2022).

24. Rahman, S., Habiba, K., Roy, S., and Nur, F.N. Job Title Prediction and Recommendation System for IT Professionals. Proceedings of the 2023 International Conference on Sustainable Technologies for Industry 5.0 (STI), (2023). https://www.researchgate.net/publication/379290273.

25. Arshkon, A. LinkedIn Job Postings. Kaggle Dataset (2023). https://www.kaggle.com/datasets/arshkon/linkedin-job-postings.