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2024 | OriginalPaper | Buchkapitel

A Hybrid Approach for Depression Detection Using Word Embedding, Naive Bayes and Bi-LSTM Models

verfasst von : Jyoti Singh, Ishan Mangotra, Minni Jain, Amita Jain

Erschienen in: Advances in Data-Driven Computing and Intelligent Systems

Verlag: Springer Nature Singapore

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Abstract

Depression is a serious illness that negatively affects health and well-being. A large population suffers from depression and they do not want to talk about the mental illness. The stigma associated with mental illness may discourage people from getting treatment thus leading to serious issues, such as social isolation, discrimination and self-harm. The high use of social media enables people to express their feeling and thoughts easily. The objective of this research is the diagnosis of depression in a person from his/her social media behaviour. The novel approach of the proposed model is to ensemble the Gaussian Naive Bayes classifier and Bi-LSTM to find contextual semantics of the text using Part-of-Speech (POS) tagging and Word Embedding. The experimental result shows the proposed model outperforms the state-of–the-art method and shows an accuracy of 83% on the benchmark dataset.

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Titel: A Hybrid Approach for Depression Detection Using Word Embedding, Naive Bayes and Bi-LSTM Models
verfasst von: Jyoti Singh
Ishan Mangotra
Minni Jain
Amita Jain
Verlag: Springer Nature Singapore
Buch: Advances in Data-Driven Computing and Intelligent Systems
Print ISBN: 978-981-9995-20-2

Electronic ISBN: 978-981-9995-21-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-9521-9_23

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