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題名 整合LIWC與LDA於心理學文本分析上之應用：以乳癌患者訪談稿為例
Integration of LIWC and LDA for text analysis in psychology: Using breast cancer patients` transcripts as an example
作者 凃昱辰
貢獻者 楊立行
凃昱辰
關鍵詞 文本分析
乳癌
創傷後成長
語文探索與字詞計算
隱含狄利克雷分佈
日期 2023
上傳時間 9-Mar-2023 18:24:35 (UTC+8)
摘要 語言是研究人類行為的重要媒介，心理學跟資料科學都是研究語言文本的領域，心理學家會先形成研究假設，再使用內容分析編碼或運用預先定義的LIWC辭典分析；資料科學家則會找出文本特徵，例如詞彙數統計，或者使用LDA分析主題。本研究欲截長補短，發展了整合LIWC與LDA的雙取向文本分析方法，使用李昀潔（2017）乳癌患者生命故事的逐字稿資料進行二次分析，並以其分析受試者高峰、低潮、轉折三個階段與創傷後成長高分組與低分組的詞類使用分析結果為對照。本研究分析一發現個體在高峰、低潮、轉折三個階段會使用不同比例的正向情緒詞、負向情緒詞、焦慮詞、生氣詞、悲傷詞、工作詞、成就詞、休閒詞、健康詞、宗教詞、認知歷程詞，結果與李昀潔（2017）相符，顯示了本研究雙取向分析的可行性與對字詞使用差異的敏感度；分析二加入創傷後成長的面向，發現創傷後成長高分組與低分組乳癌患者於三個生命故事階段會使用不同比例的第一人稱單數代名詞、相對詞、確切詞、正向情緒詞、否定詞、LSM指標，結果與李昀潔（2017）相似，惟詞類在部分階段未有顯著差異，顯示雙取向文本分析能補充比單用封閉式詞彙分析更多的細節。分析三以分析二的階段與詞類放入逐步迴歸計算，找出較能預測創傷後成長的變項為低潮確切詞、轉折正向情緒詞、轉折LSM指標、高峰第一人稱單數代名詞。本研究結果顯示整合LIWC與LDA雙取向的文本分析方法確實可以提供更多的研究啟發。
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描述 碩士
國立政治大學
心理學系
107752009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107752009
資料類型 thesis
dc.contributor.advisor 楊立行zh_TW
dc.contributor.author (Authors) 凃昱辰zh_TW
dc.creator (作者) 凃昱辰zh_TW
dc.date (日期) 2023en_US
dc.date.accessioned 9-Mar-2023 18:24:35 (UTC+8)-
dc.date.available 9-Mar-2023 18:24:35 (UTC+8)-
dc.date.issued (上傳時間) 9-Mar-2023 18:24:35 (UTC+8)-
dc.identifier (Other Identifiers) G0107752009en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143779-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 心理學系zh_TW
dc.description (描述) 107752009zh_TW
dc.description.abstract (摘要) 語言是研究人類行為的重要媒介，心理學跟資料科學都是研究語言文本的領域，心理學家會先形成研究假設，再使用內容分析編碼或運用預先定義的LIWC辭典分析；資料科學家則會找出文本特徵，例如詞彙數統計，或者使用LDA分析主題。本研究欲截長補短，發展了整合LIWC與LDA的雙取向文本分析方法，使用李昀潔（2017）乳癌患者生命故事的逐字稿資料進行二次分析，並以其分析受試者高峰、低潮、轉折三個階段與創傷後成長高分組與低分組的詞類使用分析結果為對照。本研究分析一發現個體在高峰、低潮、轉折三個階段會使用不同比例的正向情緒詞、負向情緒詞、焦慮詞、生氣詞、悲傷詞、工作詞、成就詞、休閒詞、健康詞、宗教詞、認知歷程詞，結果與李昀潔（2017）相符，顯示了本研究雙取向分析的可行性與對字詞使用差異的敏感度；分析二加入創傷後成長的面向，發現創傷後成長高分組與低分組乳癌患者於三個生命故事階段會使用不同比例的第一人稱單數代名詞、相對詞、確切詞、正向情緒詞、否定詞、LSM指標，結果與李昀潔（2017）相似，惟詞類在部分階段未有顯著差異，顯示雙取向文本分析能補充比單用封閉式詞彙分析更多的細節。分析三以分析二的階段與詞類放入逐步迴歸計算，找出較能預測創傷後成長的變項為低潮確切詞、轉折正向情緒詞、轉折LSM指標、高峰第一人稱單數代名詞。本研究結果顯示整合LIWC與LDA雙取向的文本分析方法確實可以提供更多的研究啟發。zh_TW
dc.description.tableofcontents 摘要 i
主題模型於心理學文本分析上之應用：以乳癌患者訪談稿為例 1
心理學家的文本分析 2
內容分析 2
LIWC程式分析 5
資訊科學家的文本分析 9
潛在語意分析（latent semantic analysis，簡稱LSA） 11
隱含狄利克雷分佈（latent Dirichlet allocation，簡稱LDA） 12
心理學家與資料科學家的文本分析派典比較 15
生命故事中的創傷後成長 17
研究目的 21
研究方法 23
研究資料收集流程說明 23
研究工具 24
分析一 24
方法 25
研究資料 25
研究程序 25
結果與討論 27
分析二 37
方法 38
研究資料 38
研究程序 38
結果與討論 39
分析三 56
方法 57
研究資料 57
研究程序 57
結果與討論 59
綜合討論 59
參考文獻 65		zh_TW
dc.format.extent 3669034 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107752009en_US
dc.subject (關鍵詞) 文本分析zh_TW
dc.subject (關鍵詞) 乳癌zh_TW
dc.subject (關鍵詞) 創傷後成長zh_TW
dc.subject (關鍵詞) 語文探索與字詞計算zh_TW
dc.subject (關鍵詞) 隱含狄利克雷分佈zh_TW
dc.title (題名) 整合LIWC與LDA於心理學文本分析上之應用：以乳癌患者訪談稿為例zh_TW
dc.title (題名) Integration of LIWC and LDA for text analysis in psychology: Using breast cancer patients` transcripts as an exampleen_US
dc.type (資料類型) thesisen_US
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