• Amaresh Bose

This article examines the determinants of employee attrition and proposes the application of Artificial Intelligence to forecast forthcoming causes of attrition via sentiment and thematic analysis. The main aim of this research is to investigate the factors that influence employees' choices to depart from a company and to anticipate such behavior in advance through the use of NLP (Natural Language Processing) artificial intelligence. Employee attrition is influenced by a multitude of factors, the relative importance of which differs, as determined by a study conducted at Chakan MIDC, an auto-component manufacturing company located in Pune, India. Organizations must possess the ability to proactively mitigate attrition by identifying the critical factors that carry substantial weight. Conducting research can provide valuable insights into the factors that may influence employees to voluntarily depart from an organization in the future. A comprehensive inquiry was undertaken to identify the principal determinant influencing employee attrition. By adopting an innovative and proactive approach, it is possible to develop novel work policies that are advantageous to the employee and the business organization. It might be construed as an indication of the employees' working environments.

Artificial intelligence, employee attrition, prediction model, employee sentiment analysis, thematic analysis, natural language processing

"Examining Human Resources Attrition: An Approach Through Thematic and Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a206-a214, April-2024, Available :http://www.jetir.org/papers/JETIR2404030.pdf

"Examining Human Resources Attrition: An Approach Through Thematic and Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa206-a214, April-2024, Available at : http://www.jetir.org/papers/JETIR2404030.pdf