A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ghosh et al. [114] found that a multi-task learning framework significantly boosts the efficacy of emotion recognition and depression detection when learned concurrently.
“Their findings suggest that the multi-tasking framework significantly boosts the efficacy of both tasks when learned concurrently.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [114]
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References 88 · Cited by 102
- Authors
- Ghosh, S., Ekbal, A., & Bhattacharyya, P.
- Affiliations
- Indian Institute of Technology Patna (IN)
- Corresponding author
- Ghosh, Soumitra
- Venue
- Cognitive Computation
- Published
- 2021-02-05
- Volume
- 14
- Issue
- 1
- Pages
- 110-129
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W3127582740
- Cited by
- 102
- References
- 88
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 24 · 2023: 24 · 2022: 22 · 2021: 9
- FWCI
- 19.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Computation (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- suicide notes, depression detection, sentiment analysis, multi-task learning, weakly supervised learning, SenticNet, suicidal ideation, mental health
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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