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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成 | zh_TW |
dc.contributor.advisor | Li-Chen Fu | en |
dc.contributor.author | 謝賀淇 | zh_TW |
dc.contributor.author | Ho-Chi Hsieh | en |
dc.date.accessioned | 2023-10-03T17:42:37Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-08 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90807 | - |
dc.description.abstract | 憂鬱症是一種常見的心理疾病,對患者的日常生活產生了很大的影響。然而, 由於社會文化或經濟壓力等原因,許多人可能不敢尋求心理諮詢,從而導致憂鬱 症問題加劇或延誤就診時間。因此,開發一個能夠隨時檢測自身心理狀態的憂鬱 症偵測系統至關重要。本論文提出了一種基於深度學習技術實現的憂鬱症偵測模 型,利用社交型機器人與使用者進行交流,提供情緒支持和安撫的回應方式,讓 使用者能更自由地表達內心感受。
目前,大多數與憂鬱症相關的方法主要關注於憂鬱症偵測的預測能力,但未 能考慮實際應用場景。因此,本論文提出了一種基於圖神經網路的架構,該架構 利用使用者完整的對話紀錄作為輸入,結合單詞和句子的多方面語義特徵,同時 整合了情緒和生理特徵等額外資訊,以更全面地預測憂鬱症。此外,在對話過程 中,系統以具有情緒輔助能力的回覆方式與使用者進行交流。當對話結束時,系 統根據對話內容生成對應的偵測結果,並以自然語言的方式回覆,以提高使用者 與系統之間的流暢性。因此,本論文提出的社交型機器人不僅能檢測憂鬱症,還 能提供情緒安撫和支持,使使用者更好地了解自己的心理狀態。 | zh_TW |
dc.description.abstract | Depression is a common mental disorder that has a significant impact on the day-to-day life of individuals. Due to social, cultural, or economic pressures, many people are reluctant to seek psychological counseling, resulting in worsening depression or delayed treatment. Therefore, it is crucial to develop a depression detection system. This system should be able to monitor one’s psychological state at any time. In this thesis, a depression detection model based on deep learning techniques is proposed, which uses a social robot to engage in conversations with users and to provide emotionally supportive and comforting responses, thus allowing users to freely express their inner feelings.
Currently, most of the depression detection methods mainly focus on the predictive capability of depression detection without consideration of practical application scenarios. Therefore, an architecture based on graph neural networks using complete user conversation records as input is presented in this thesis To comprehensively predict depression, this architecture integrates various semantic features of words and sentences as well as additional information such as emotional and biological characteristics. In addition, the system interacts with the user during the call by responding with emotionally comforting responses. At the end of the conversation, the system generates appropriate recognition results based on the content of the conversation and replies in natural language to enhance the communication between the user and the system. In this way, the social assistant robot proposed in this thesis not only detects depression but also provides emotional support and comfort, enabling the user to have a better understanding of his or her mental state. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:42:37Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T17:42:37Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝 i
摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES viii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Related Work 3 1.3.1 Depression Detection 4 1.3.2 Emotional Support Conversation 6 1.3.3 Comparison 7 1.4 Objectives and Contributions 8 1.5 Thesis Organization 9 Chapter 2 Preliminaries 11 2.1 Depression 11 2.2 Neural Networks 13 2.2.1 Graph Neural Network 14 2.2.2 Transformer 16 2.3 Neural Language Generation 17 2.3.1 Statistical Approach 18 2.3.2 Learning-based Method 19 Chapter 3 Methodology 21 3.1 System Overview 21 3.2 Dialogue Context Creator 23 3.3 Text Preprocessing 25 3.4 Depression Detection 27 3.4.1 Emotion Recognition 29 3.4.2 Graph Constructor 32 3.4.3 Fusion 34 3.5 Emotional Support Generator 37 3.5.1 Prompt Generator 37 3.5.2 Large Language Model 42 3.5.3 Response Prompt 43 Chapter 4 Experiments 45 4.1 Experimental Setup 45 4.1.1 Datasets 45 4.1.1.1 Depression Detection Dataset 45 4.1.1.2 Emotional Support Dataset 46 4.1.2 Evaluation Metrics 48 4.1.3 Comparison Methods 52 4.1.4 Implementation Details 53 4.2 Depression Detection Results and Discussion 53 4.2.1 Qualitative Results 54 4.2.2 Ablation Study 55 4.3 Emotional Support Results and Discussion 57 4.3.1 Automatic Evaluation Results 58 4.3.2 Human Evaluation 60 4.4 Human System Interaction 61 Chapter 5 Conclusion 67 REFERENCES 69 | - |
dc.language.iso | en | - |
dc.title | 利用深度學習檢測憂鬱症與情緒輔助之社交機器人 | zh_TW |
dc.title | Deep Learning based Depression Detection and Emotional Support with Social Assistant Robot | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 蘇木春;葉素玲;張玉玲;陳縕儂 | zh_TW |
dc.contributor.oralexamcommittee | Mu-Chun Su;Su-Ling Yeh;Yu-Ling Chang;Yun-Nung Chen | en |
dc.subject.keyword | 憂鬱症,自動化偵測,圖神經網路,情緒安撫,對話系統, | zh_TW |
dc.subject.keyword | Depression,Automated Detection,Graph Neural Networks,Emotional Support,Dialogue System, | en |
dc.relation.page | 74 | - |
dc.identifier.doi | 10.6342/NTU202303356 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-09 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
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