文章基本信息

标题：Psychological Analysis for Depression Detection from Social Networking Sites
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作者：Sonam Gupta ; Lipika Goel ; Arjun Singh 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/4395358
语种：English
出版社：Hindawi Publishing Corporation
摘要：Rapid technological advancements are altering people’s communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person’s onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers—decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM—for depression detection in tweets. The dataset is collected in two forms—balanced and imbalanced—where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.