Detection of depression in Tweets using Sentiment Analysis A Machine Learning Approach
Abstract
Statistical data by world health organization (WHO) reveals that depression is the major cause for many of the social misadventure. Patients with depression do use the modern day social networking to express their emotion. Selective analysis of social networking data will result in determining the degree of depression and plausible psychiatric solutions. In this paper, a machine learning based model has been proposed for sentiment analysis from social networking data. The proposed model uses support vector m/c, a decision tree classifier, and a Bayesian classifier. Performance analysis of the proposed model was evaluated by Python simulation using Count vectorizer and decision tree classifier.
Keywords
Depression, SVM, Decision tree, Naïve classifier, Vectorizer