Identifying suicidal emotions on social media through transformer-based deep learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04060-8.pdf
Reference75 articles.
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2. Caicedo R, Gómez J, Sasieta H (2020) Assessment of supervised classifiers for the task of detecting messages with suicidal ideation. Heliyon 6:04412. https://doi.org/10.1016/j.heliyon.2020.e04412
3. Platt S, Arensman E, Rezaeian M (2019) National suicide prevention strategies – progress and challenges. Crisis 40:75–82. https://doi.org/10.1027/0227-5910/a000587
4. Ji S, Yu C, Fung S-f, Pan S, Long G (2018) Supervised learning for suicidal ideation detection in online user content. Complexity 2018:1–10. https://doi.org/10.1155/2018/6157249
5. Sarsam S, Al-Samarraie H, Alzahrani A, Alnumay W, Smith A (2021) A lexicon-based approach to detecting suicide-related messages on twitter. Biomed Signal Process 65:102355. https://doi.org/10.1016/j.bspc.2020.102355
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