ID | 118923 |
著者 |
Dou, Rongyu
Tokushima University
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キーワード | Neuro-Symbolic AI
Depression detection
Social media analysis
Early intervention
Sentiment analysis
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資料タイプ |
学術雑誌論文
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抄録 | This paper introduces TAM-SenticNet, a Neuro-Symbolic AI framework uniquely designed for early depression detection through social media content analysis. Merging neural networks for feature extraction and sentiment analysis with advanced symbolic reasoning, TAM-SenticNet overcomes the limitations of traditional diagnostic tools, particularly in real-time responsiveness and interpretability. The symbolic reasoning, powered by SenticNet, provides a deep, structured understanding of emotional expressions, greatly enhancing model explainability and logical inference. Empirical evaluations reveal that TAM-SenticNet excels beyond existing models in performance metrics, achieving a Precision of 0.665, Recall of 0.881, and F1-score of 0.758, coupled with superior latency metrics, including ERDE5 and ERDE50 at 0.025, LatencyTP at 1.0, and Flatency at 0.675. These achievements highlight TAM-SenticNet’s cutting-edge approach to early depression detection, making it a pioneering tool in the application of AI for mental health informatics.
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掲載誌名 |
Computers and Electrical Engineering
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ISSN | 18790755
00457906
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cat書誌ID | AA00613548
AA11527073
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出版者 | Elsevier
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巻 | 114
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開始ページ | 109071
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発行日 | 2024-01-20
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備考 | 論文本文は2026-01-20以降公開予定
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権利情報 | © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
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EDB ID | |
出版社版DOI | |
出版社版URL | |
言語 |
eng
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著者版フラグ |
その他
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部局 |
理工学系
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