Advanced risk assessment using machine learning and sentiment analysis on log data - Turab, Nidal; Abushattal, Abdelrahman; Al-Nabulsi, Jamal; Owida, Hamza Abu (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.