Hybrid machine learning models to detect signs of depression-Reference-Cited by-同舟云学术

Hybrid machine learning models to detect signs of depression

Published:2023-10-06 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Khan Shakir,Alqahtani Salihah

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16221-z.pdf

Reference21 articles.

1. Arora P, Arora P (2019) Mining Twitter data for depression detection. In: 2019 international conference on signal processing and communication (ICSC)

2. Azam F, Agro M, Sami M, Abro MH, Dewani A (2021) Identifying depression among Twitter users using sentiment analysis. In: 2021 international conference on artificial intelligence (ICAI)

3. Chiu Y, Lane HY, Koh JL, Chen AL (2020) Multimodal depression detection on Instagram considering time interval of posts. J Intell Inf Syst 56(1):25–47

4. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT 1:4171–4186

5. Garg M Sentimental Analysis for Tweets, Kaggle, 03-May-2021. [Online]. Available: https://www.kaggle.com/gargmanas/sentimental-analysis-for-tweets. Accessed 1 Feb 2022

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