Depression Detection Using Machine Learning

The issue of depression in our society is becoming increasingly significant as more and more people are affected by it. It's a debilitating disorder that can ‘affect people of all ages, and while some may recognize that they have it, others may be unaware. Individuals have started using social media as a platform to record their emotional states, and studies have been carried out utilizing machine learning algorithms to identify depression by analyzing social media posts. By analyzing data available on social media (Twitter), it is possible to determine whether a person is experiencing depression or not. Machine learning algorithms are used to classify the data and identify depressive and non-depressive posts. This proposed system uses Twitter data and various algorithms, such as XGBoost, SVM, Logistic Regression, and Random Forest Classifier, to detect depression. The results were compared based on the highest accuracy level, and it was found that the XGBoost algorithm had the highest accuracy level. A User Interface is introduced for real time classification