Exploring Machine Learning Algorithms for the Detection of Depression

In this digital world, the excessive use of social media is increasing the incidence of mental health problems, especially depression. Due to the widespread usage of online social media platforms, there is now a chance to proactively treat mental health issues by observing and comprehending users’ behavior. Early detection of depression symptoms is essential for prompt assistance and management. By employing advanced artificial intelligence techniques and machine learning algorithms, such as Long Short-Term Memory and Support Vector Machine, this system effectively analyzes users’ social media behavior to determine whether they are experiencing depression and acts as a tool for the early identification of mental health problems, supporting a proactive strategy for enhancing people’s well-being in the digital era. By mining users’ social media interactions and classifying them as potentially depressed or not, this approach helps those in need in a timely manner and delivers insightful information on people’s mental health. Furthermore, the applications of this system extend beyond detecting and preventing online bullying; it can also be used to detect and manage other mental health issues, which in turn helps in emotional well-being. These ideas can be employed in social media applications to check mental stability. This chapter emphasizes the crucial role of textual emotional intelligence in mining users’ social media behavior for detecting depression and other mental health issues. By employing cutting-edge artificial intelligence techniques and machine learning algorithms, this system offers valuable insights and assistance to individuals in need and also helps in maintaining proper e-health.