Classification of Depression on social media using Distant Supervision

Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues been spiking all over the world rapidly. People taken up to social media as a platform vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets these times, but downside being that majority population neglected importance there are not enough resources liberate people it. Also, hesitate talk seek help. So, machine learning model using distant supervision detect on is curated. Use Sentiment140 dataset with 1.6 million records different tweets. Our training data makes use included emojis, which classified noisy labels dataset. Further, this paper mentions how models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost distinguishing between or nondepressive. The purpose behind multiple achieve highest accuracy when trained emoticon paper’s main contribution idea emoticons for supervised learning.