Please use this identifier to cite or link to this item: http://192.168.9.248:8080/jspui/handle/123456789/569
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dc.contributor.authorJain, Shikha ed.
dc.contributor.authorRoshan Jahan
dc.contributor.authorTripathi, Manish Madhav
dc.date.accessioned2023-09-02T05:50:57Z
dc.date.available2023-09-02T05:50:57Z
dc.date.issued2022
dc.identifier.isbn978-0-323-91196-2
dc.identifier.urihttps://doi.org/10.1016/C2020-0-04085-5
dc.identifier.urihttp://192.168.9.248:8080/jspui/handle/123456789/569
dc.descriptionChapter Two - Multimodal depression detection using machine learning by Roshan Jahan, and Manish Madhav Tripathien_US
dc.description.abstractDepression is a mood disorder that includes feelings of sadness, loss, or anger. It interferes with a person's daily activities. People express frustration in various ways. Some people react on social media whereas other people react in their personal lives. People use social media to share information and chat with friends. This creates a huge amount of data each day. These data can be gathered in the form of images, videos, and text reflecting the mental status of the person. However, researchers are working to employ computational models on user-generated content to learn patterns automatically, although much small-scale research has been conducted by assuming that the unimodality of data may not bring us truthful results. This chapter explains various models of machine learning to detect depression. The models are implemented to analyze emotions using Twitter and Facebook posts.en_US
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.subjectComputer Science and Engineeringen_US
dc.titleArtificial Intelligence, Machine Learning, and Mental Health in Pandemics: a computational approachen_US
dc.typeBook chapteren_US
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