政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151094

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151094

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/151094

Title:	安心專線對話過程中語句累積對自然語言處理模型於自殺風險預測之研究 Research on the Impact of Incremental Utterance Input During Taiwan Lifeline Conversations on Natural Language Processing Models for Suicide Risk Prediction
Authors:	胡鈞彥 Hu, Chun-Yen
Contributors:	游琇婷 Yu, Hsiu-Ting 胡鈞彥 Hu, Chun-Yen
Keywords:	安心專線自殺風險預測語句累積輸入自然語言處理 LLMs BERT Vicuna Taiwan Lifeline suicide risk detection incremental utterance input natural language processing LLMs BERT Vicuna
Date:	2024
Issue Date:	2024-05-02 10:34:13 (UTC+8)
Abstract:	自殺是全球重要的公共衛生議題之一，因其不僅影響個人及家庭，也可能影響國民心理健康與經濟發展，增加社會成本。有鑑於此，對自殺風險的即時偵測與介入顯得尤為重要。過去研究利用自然語言處理技術進行自動化自殺風險偵測，但多基於線上平台的資料，如社交媒體或論壇貼文，較缺乏使用真實對話資料進行分析的研究。安心專線是台灣提供線上心理協談服務的通話專線，其資料性質不同於一般的「靜態」文本資料，對話中即時互動提供更豐富的心理訊息，因此本研究以安心專線通話資料進行自殺風險偵測分析，使用將通話記錄謄寫成逐字稿的文本資料進行自殺風險評估。此資料包含求助者與接線員間的對話文字記錄，以及由臨床心理學專家判定的求助者自殺風險等級。本研究主要關注兩個問題：一是如何利用通話中語句隨時間推進的特性來進行更精確的自殺風險評估；二是使用SBERT計算句子相似性時，自殺量表與自殺關聯詞典等不同的參照語句對自殺風險偵測模型的性能有何影響。本研究預期能更了解自然語言處理模型在不同對話階段的表現，且增進模型對自殺風險的早期識別能力，從而提供更即時的協助給自殺防治的實務工作者。 Suicide constitutes a major public health concern worldwide, affecting not only individuals and their families but also national mental wellbeing and economic growth, which adds to societal costs. Therefore, the immediate detection and intervention of suicide risk are imperative. Past research has employed natural language processing (NLP) for automated detection of suicide risk, mostly relying on data from online platforms such as social media or forums, lacking studies using actual conversational data. In Taiwan, the Lifeline hotline provides online psychological counseling, and its data, unlike standard ‘static’ text, contains real-time interactive dialogue offering deeper psychological insights. Therefore, this research utilizes transcribed data from these calls for suicide risk detection analysis. The dataset includes transcripts of conversations between callers and counselors, evaluated by a clinical psychologist for suicide risk levels. This study addresses two main issues. First, utilizing the temporal progression of sentences in calls for more precise suicide risk assessments. Second, examining how using SBERT to compute sentence similarity with various reference sentences, such as suicide scales and suicide-related dictionaries, affects the performance of suicide risk detection models. The objective is to enhance understanding of NLP models across different stages of dialogue and improve early detection capabilities of suicide risk, ultimately providing timely aid and information to frontline workers in suicide prevention.
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Description:	碩士國立政治大學心理學系 110752002
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110752002
Data Type:	thesis
Appears in Collections:	[Department of Psychology] Theses

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