Big data analytics on social networks to detect melancholy in real-time

During the coronavirus pandemic, the wide variety of despair instances has dramatically increased. Several melancholy sufferers disclose their true feeling with the aid of social media. Thus, large records analytics on social networks for real-time despair detection is proposed. Machine mastering techniques were utilized as the detection mannequin construction. There are five computing device mastering strategies explored in this research which are Support Vector Machine, Decision Tree, Naïve Bayes, Random Forest, and Deep Learning. The experimental outcomes revealed that the Random Forest technique performed higher accuracy than other methods to become aware of the depression. Keywords: Big data analytics, Depression detection, social networks