Sentiment analysis for predicting stress among workers and classification utilizing CNN: Unveiling the mechanism

Abstract

Stress is one of the main factors that affect the lives of many individuals' lives. Stressful work situations cause significant levels of stress in many employees. Extreme stress can cause significant problems at work and put workers' productivity, health, and safety at risk. Therefore, it is crucial to inform the person about his or her risky lifestyle and forewarn him or her before a serious issue arises. This study offers a novel optimized deep neural network technique, ABC-ABO-CNN, to forecast worker stress. Individuals are significantly impacted by stress, especially in high-stress job contexts. Identifying and measuring employee stress levels is critical to maintaining productivity, health, and safety. Traditional methods like self-assessment questionnaires have limitations, leading to biased results. The suggested approach uses a Convolutional Neural Network (CNN) in conjunction with the African buffalo and Artificial Bee Colony optimization algorithms to overcome these drawbacks. Physiological information such as heart rate, BMI, pressure, glucose level, and skin reaction is used to detect stress. The next step is to categorize stress levels using sentiment analysis. When the suggested mechanism's performance is compared to that of traditional methods, it is shown to have greater accuracy, stress detection rate, and precision values.