Early Depression Detection Using Textual Cues from Social Data: A Research Agenda-Reference-Cited by-同舟云学术

Early Depression Detection Using Textual Cues from Social Data: A Research Agenda

Published:2023 Issue: Volume: Page:393-406
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Dalal Sumit,Jain Sarika,Dave Mayank

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-9090-8_34

Reference26 articles.

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2. Depression and Other Common Mental Disorders (2017) Global health estimates. World Health Organization. https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf. Accessed: 2022-01-30

3. Coronavirus data & statistics. https://www.cdc.gov/datastatistics/index.html. Accessed: 2022-01-12

4. Bhatia S, Kesarwani Y, Basantani A, Jain S (2021) Engaging smartphones and social data for curing depressive disorders: an overview and survey. In: Proceedings of the international conference on paradigms of computing, communication and data sciences. Springer, pp 809–820

5. Dalal S, Jain S (2021) Smart mental healthcare systems. In: Web semantics. Elsevier, pp 153–163

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1. Convolution Neural Network Having Multiple Channels with Own Attention Layer for Depression Detection from Social Data;New Generation Computing;2023-11-01

2. Detecting Depression Level Scale from Social Media Text and Images Using Machine Learning Algorithms;2023 6th International Conference on Contemporary Computing and Informatics (IC3I);2023-09-14