Research Area: Machine Learning
Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text classifiers are trained for depression detection. The key objective is to improve depression detection performance by examining and comparing two sets of methods: hybrid and ensemble. The results show that ensemble models outperform the hybrid model classification results. The strength and effectiveness of the combined features demonstrate that better performance can be achieved by multiple feature combinations and proper feature selection.
Keywords:
Feature extraction
Depression
Social networking (online)
Hidden Markov models
Data models
Neural networks
Linguistics
Author(s) Name: Luna Ansari; Shaoxiong Ji; Qian Chen;
Journal name: IEEE Transactions on Computational Social Systems
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TCSS.2022.3154442
Volume Information: Volume: 10
