Depression Detection from Social Site using Machine Learning and Deep Learning

Abstract

As COVID-19 crisis is settling down in countries, whether or not a person has been affected personally by the disease, he fights with issues such as anxiety, panic attacks, grief, low mood, and many other psychotic disorders. Mental fitness is one of the major strengths in the development of the individual. Development of social sites turns out to be one platform where the person feels free to vent out their thoughts and to easily interact with people. Extracting useful information from those posts is a part of sentimental analysis, which is the technique of machine learning that helps to know the mental condition of the individual. In this paper, various machine learning algorithms such as random forest, Naive Bayes, decision tree, multilayer perceptron, maximum entropy, KNN, gradient boosted decision tree, adaptive boosting, bagged logistic regression, tree ensemble model, Liblinear, convolutional neural network, and long short-term memory are applied on the dataset, and different mathematical scales such as accuracy, precision, recall, and F1 score concluded that bagged logistic regression has given the better accuracy results.