Comparative Analysis of Psychological Stress Detection: A Study of Artificial Neural Networks and Cat Boost Algorithm.

Abstract

Many people are suffering from stress and anxiety as a direct result of the rapid development of technology, especially the meteoric rise of social media. These medical problems need comprehensive analysis and the creation of reliable preventative measures. The need to control and track all the data being produced in SMEs, or social media environments, is an urgent one. Humans' natural inclination to use these kinds of sites adds an extra layer of difficulty. Psychologists have often used tools like questionnaires and interviews to investigate and treat such concerns. But these approaches take too long and are too retroactive to provide timely fixes. This study conducted a thorough analysis of several stress detection algorithms that claimed to be able to deduce emotional distress from online posts but ultimately concluded that such methods were mainly unsuccessful. The research introduces a novel strategy, an Effective Stress Detection setup. The technology uses ontology, a kind of term matching used in search engines, to identify signs of stress in social media users. We are able to more precisely detect stress-related communication on social media platforms with the use of ontology, a term generally referring to a framework that allows data classification. Using ontology, the system is able to thoroughly analyze user-generated material for signs of stress. This method has potentially life-saving implications since it not only enables early diagnosis of stress but also begins the required preventative procedures that might keep users from sinking into deep despair or even committing suicide. This study highlights the need of considering how machine learning and data science might be used to enhance mental health care in the internet age.