Sentiment Analysis and Machine Learning in Understanding Work-from-Home Stress and Work-Life Balance Among IT Professionals

The effects of online training, work-from-home stress, emotional intelligence, and work-life balance on IT Employees. Since the adoption of the new normal working, new(Address unspecified) working challenges such as stress and work-life imbalance have emerged. It uses modern advanced approaches in data analysis, such as sentiment analysis and machine learning to employee opinion in detail. To ensure the accuracy the used preprocessing methods are normalization, tokenization, and feature extraction in the given model. For the comparative analysis, both accuracy and F1 score are up to 92.4% %, while traditional models are 87.2% accurate with an F1 score of 0.895. Research shows that with high EI, there is a positive increase in the quality of work-life balance, thus a promising area to develop online training enhances productivity. These findings provide useful implications for managers and policymakers in how organizational remote work structures and employee support mechanisms should be developed. Thus, this research provides a novel theoretical and methodological approach based on the developed model for analyzing a range of emerging remote work dynamics' impact on the well-being of IT employees.