Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions

The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.

Automated summary

The coronavirus pandemic has caused a surge in depression cases globally, leading many people to use social media as an outlet to express their depression or suicidal thoughts. This study aims to analyze Twitter posts and identify features that may indicate symptoms of depression among online users. A numerical metric based on the sentiment value of tweets is proposed, and it is demonstrated that this feature, combined with machine learning classifiers, can accurately detect depression. The paper also introduces a novel method of measuring an individual's mental health index by combining sentiment scores with multimodal features extracted from their online activities, allowing for real-time monitoring and information about their mental state.