Automatic detection of safety requests in web and mobile applications using natural language processing techniques

Abstract

Web and mobile applications have become an essential part of our daily lives. However, as the usage of these applications increases, so does the potential for safety concerns. It is crucial for application developers to ensure that their applications are safe and secure for users. One way to achieve this is through the identification and processing of safety requests made by users. This research paper proposes a method for identifying safety requests made by users in web and mobile applications using natural language processing (NLP) and deep learning techniques. The approach involves training a machine learning and deep learning model on a dataset of user requests to identify and classify safety requests. The models are then integrated into the application’s code to automatically detect and respond to safety requests. A case study on a ride-sharing application showed that the proposed approach achieved high accuracy in identifying safety requests, with an F1 score of 0.85. The proposed method can be applied to vari- ous web and mobile applications to improve safety and security, and reduce the workload of manual safety request processing.