Exploring Machine Learning Approaches for Stress Detection in Reddit Content: An Evaluation of Performance and Effectiveness

Depression is a serious mental health condition that is difficult to diagnose, particularly in social media settings. The goal of this research is to enhance early intervention and prevention efforts by creating a machine learning model for identifying depression in Reddit posts. Evaluating the efficacy of various machine learning methods in stress detection is the aim of this research project. By examining Reddit Contents, this assessment seeks to advance the creation of an automated system for the early diagnosis of depression. ‘clean text’ is the raw text data from Reddit posts, and ‘is depression’ is the target variable in this dataset that was gathered for the study. To extract features, count vectorization is used in this instance. Using the training and testing sets, four machine learning algorithms-SVM, Random Forest, Naive Bayes, and KNN are trained and assessed. By utilizing a VotingClassifier with hard voting, an ensemble model is constructed. With 99.93% accuracy on the training set and ${9 5. 5 4 \%}$ accuracy on the test set, the Random Forest model proved to be the most accurate of all the models. This study shows how machine learning algorithms and ensemble approaches can be used to identify depression in Reddit posts. The ensemble model offered a marginally improved accuracy, although the Random Forest and SVM models fared the best. These findings suggest that machine learning is a useful technique for helping mental health professionals recognize and treat depression in virtual environments.