Harnessing Random Forest Classifiers and TF-IDF Vectorization: A Text-Based Approach to Detect Stress and Analyze Mental Health Using Artificial Intelligence

As we live in the digital age, the importance of stress detection research lies in the fact that people's interactions on social networking platforms and messaging services accurately reflect their emotional moods and mental health. Similarly to that, an attempt to develop an ML model that can identify human stress levels is made by analyzing the written records obtained from social media. This proposed system chooses a keyword-based method to detect a specific stress indicator statement and compare it with a given data set. The textual data are then preprocessed and vectorized based on the TFIDF method to detect meaningful patterns in the text data. The data were classified as stressed or non-stressed by a Random Forest Classifier with probabilities indicating the intensity of stress. To generate valuable insights by listening to the people's voices, which could help detect stress-based disorders in the early stages, this approach works with data obtained from online sources in compliance with ethical standards and general rule privacy. In conclusion, this study illuminates how AI-driven systems can enhance mental health awareness and intervention by implementing a holistic stress detection instrument using natural language processing approaches and machine learning methodologies.