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Predicting Depression From Social Media Users by Using Lexicons and Machine Learning Algorithms
Abstract
Depression is one of the most common health issues among individuals. The rate of psychotic treatments is increasing day by day. Depression is created in many ways among the people, especially through work stress, financial burden, unemployment among the adults. Today, the emergence of social media into people's lives makes them expose their feelings and emotions on the social media platforms. The aim of this work is to predict the depressive features from social media users' comments by using machine learning techniques. Multinomial naïve bayes, non-linear support vector machine, and artificial neural network methods are used for classifying the features and comparing it using performance evaluation metrics and get the best classifier. This system includes data pre-processing, feature extraction, data splitting, classification, and performance evaluation. The results show that the proposed system has gradually improved performance accuracy. According to the results, ANN gives 99.19%, the best accuracy compared to other machine learning classifiers.
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