Stress Prediction Using CNN: A Deep Learning Approach for Employee Well-Being Analysis

Abstract

Workplace stress is a critical factor affecting employee productivity, mental well-being, and overall job satisfaction. This project presents a deep learning-based stress prediction model using Convolutional Neural Networks (CNN) to analyze employees' textual feedback and determine their emotional state. The system processes text input provided by workers, classifies their emotions into categories such as happy, sad, stressed, ext…and predicts the intensity of stress levels. The CNN model is trained on a labeled dataset containing textual expressions of different emotional states, utilizing Natural Language Processing (NLP) techniques for feature extraction. By leveraging CNN's ability to capture complex text patterns, the model provides an accurate and efficient stress-level assessment. This solution can help organizations monitor employees' emotional well-being, enabling proactive interventions to improve workplace mental health.