Depression Detection from Twitter Data Using Two Level Multi-modal Feature Extraction

Samanta, Poulomi; Kumar, Piyush; Dutta, S.; Chatterjee, Moumita; Sarkar, Dhrubasish

doi:10.1007/978-981-19-2600-6_32

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Although significant progress has already been achieved in this subject, there are still certain obstacles to overcome. In our previous studies (Chatterjee et al, 2022a;Kumar et al, 2022;Chatterjee et al, 2022b;Samanta et al, 2022;Sarkar et al, 2022;Sarkar et al, 2023), we aimed to attain a high performance in the early depression diagnosis by carefully selecting features from Twitter tweets. In this study, we intend to extend our prior research on depression detection by aiming to continually track a person's mental health and provide real-time information about his condition.…”

Section: Introductionmentioning

confidence: 99%

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

Chatterjee

Kumar²,

Sarkar

2023

International Journal of Intelligent Information Technologies

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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.

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Section: Introductionmentioning

confidence: 99%

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

Chatterjee

Kumar²,

Sarkar

2023

International Journal of Intelligent Information Technologies

Self Cite

View full text Add to dashboard Cite

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Mental Health Predictions Through Online Social Media Analytics

Chatterjee

Modak

Sarkar

2023

Cognitive Cardiac Rehabilitation Using IoT and AI Tools

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According to WHO data, mental health is a major source of concern throughout the world, amounting to suicide in the majority of instances if left untreated. Presently, social media is a great way for people to express themselves through text, emoticons, images, or videos that depict their sentiments and emotions. This has opened up the possibility of investigating social networks in order to better comprehend their users' mental states. Social media is currently being used by researchers to predict the prevalence of mental illnesses such as depression, suicide ideation, anxiety, and stress. This area of study has considerable potential for the monitoring, diagnosis, and prevention of mental health issues. In this research we aim to give an overview of the most recent works for predicting mental health status on social media. We focused on data collection and annotation approaches, preprocessing and feature selection, model selection, and validation. We addressed research on depression and suicidal thoughts, which are the most common among mental health studies on social media.

show abstract

Depression Detection from Twitter Data Using Two Level Multi-modal Feature Extraction

Cited by 2 publications

References 18 publications

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

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

Mental Health Predictions Through Online Social Media Analytics

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