An Ensemble-Based Machine Learning Model for Emotion and Mental Health Detection

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dc.contributor.authorJonnalagadda, Annapurna
dc.contributor.authorRajvir, Manan
dc.contributor.authorSingh, Shovan
dc.contributor.authorChandramouliswaran S.
dc.contributor.authorGeorge, Joshua
dc.contributor.authorKamalov, Firuz
dc.date.accessioned2023-03-22T11:45:31Z
dc.date.available2023-03-22T11:45:31Z
dc.date.copyright© 2023
dc.date.issued2022
dc.description.abstractRecent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person's emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person's emotions. However, these solutions often rely on techniques that invade people's privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people's emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user's typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user's emotions. The proposed solution has various applications in today's world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members. © 2023 World Scientific Publishing Co.
dc.identifier.citationJonnalagadda, A., Rajvir, M., Singh, S., Chandramouliswaran, S., George, J., & Kamalov, F. (2022). An ensemble-based machine learning model for emotion and mental health detection. Journal of Information and Knowledge Management, https://doi.org/10.1142/S0219649222500757
dc.identifier.issn02196492
dc.identifier.urihttps://doi.org/10.1142/S0219649222500757
dc.identifier.urihttps://hdl.handle.net/20.500.12519/795
dc.publisherWorld Scientific
dc.relationAuthors Affiliations : Jonnalagadda, A., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Rajvir, M., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Singh, S., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Chandramouliswaran, S., School of Information Technology and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; George, J., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesJournal of Information and Knowledge Management
dc.rights.holder© 2023 World Scientific Publishing Co.
dc.subjectK-Neighbours classifier
dc.subjectkeystroke dynamics
dc.subjectlogistic regression
dc.subjectrandom forest
dc.subjectsupervised machine learning
dc.subjectsupport vector machine
dc.subjectvoting classifier
dc.titleAn Ensemble-Based Machine Learning Model for Emotion and Mental Health Detection
dc.typeArticle
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