Predicting workspace satisfaction: A Machine Learning Study on Employee Review

by Jahnvi Singh, Biky Chouhan, Bishwajeet Pandey

This research uses machine learning methods to determine influential factors driving job satisfaction articulated by means of employee comments. From studying extensive data collections, the research indicates trends and prognostic drivers behind workers' satisfaction and discontent. Methods used to compute algorithms in evaluating written information derived from workers' commentaries within and outside industrial workplaces include sentiment analysis, NLP, and regression algorithms.
The findings yield practical recommendations to employers to help maximize workplace happiness. The principal determinants of satisfaction, like the quality of leadership, balance between work and life, rewards, and prospects for growth, are identified. Common sources of dissatisfaction are on the other hand examined to present suggestions for remedies. Through highlighting these findings, the research draws attention to evidence-based decision-making to create great working environments and retain employees.

Kindle Edition

Published May 7, 2025