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題名 文本情緒推論：開放式與封閉式詞彙分析
Textual Emotion Inference: Closed- vs. Open- Vocabulary Analyses
作者 尤譯霆
Yu, Yi-Ting
貢獻者 楊立行
尤譯霆
Yu, Yi-Ting
關鍵詞 文本分析
情緒推論
開放式詞彙分析
封閉式詞彙分析
日期 2023
上傳時間 6-Jul-2023 16:57:24 (UTC+8)
摘要 人類是群性的物種，而語言正是群體機制運作中不可或缺的情報資訊。近年以心理建構主義（psychological constructionist）為基礎的概念行動理論（Conceptual Act Theory）強調人們對於理解語言中的情緒訊息依據可以是情緒詞彙，也可以是讀者自身攜帶情緒意涵的情節知識。心理學家一直希望從人們的話語中去了解個體的思考與情緒內容，然而，直到本世紀初拜電腦技術的進展，心理學家才開發出突破性的電腦化文本分析技術，該技術仰賴將不同詞彙歸類至數個詞類之中，並經過大規模的評定以確立它們作為文本分析效標的信效度。由於有事先定義好的詞類，因此研究者稱其為封閉式詞彙分析。封閉式詞彙分析其最大缺點在於，同一詞彙解釋方式都是固定的，並忽略了上下文脈絡。根據概念行動理論，文本分析中情緒詞類出現的頻次，在推論文本情緒價性時應能有一定程度的助益。然而，封閉式詞彙分析去除文本脈絡也可能會減損其推論文本情緒時的檢定力（power）。同時資訊科學家亦致力於讓機器具有理解人類語文的智能，受惠於大數據時代的來臨以及各類機器學習演算法的精進，資訊科學家研發的文本分析技術已經證明可透過大量文本有效抽取出其潛在特徵，這類沒有事先評定好的文本分析方式稱為開放式詞彙分析。由於不侷限於事先決定好的詞類，因此開放式詞彙分析可能會比較有機會掌握文本內上下文語義的脈絡。然而，開放式詞彙與封閉式詞彙分析的比較研究著實不多，多數情況仍是各自在各自領域內發展。本論文透過兩者的比較以此說明人類理解文本情緒意涵時，最重要的語義表徵是什麼。首先透過研究一擴大詞類範圍進行檢驗，發現蒐集更多可能參與表達情緒的詞彙有助於情緒推論的正確率，但效率不彰且提升幅度不大。顯示人類理解文本情緒意涵時的基本單元並非以詞類為基礎的語義表徵。研究二則比較兩種不同開放式詞彙分析，結果顯示人類理解文本情緒意涵時，最重要的是能夠保留上下脈絡的語義表徵。最後透過研究三對封閉式詞彙分析的改良嘗試，鞏固研究二的結果。
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黃金蘭、Chung, C. K.、Hui, N.、林以正、謝亦泰、程威詮、Lam, B.、Bond. M., Pennebaker, J. W.（2012）。中文版「語文探索與字詞計算」詞典之建立。中華心理學刊，54(2)，185–201。http://dx.doi.org/10.6129/CJP.2012.5402.04
楊立行、許清芳（2019）。社群媒體上分手文章的性別差異：文本分析取徑。中華心理學刊，61(3)，209–230。https://doi.org/10.6129/CJP.201909_61(3).0003
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描述 碩士
國立政治大學
心理學系
108752001
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108752001
資料類型 thesis
dc.contributor.advisor 楊立行zh_TW
dc.contributor.author (Authors) 尤譯霆zh_TW
dc.contributor.author (Authors) Yu, Yi-Tingen_US
dc.creator (作者) 尤譯霆zh_TW
dc.creator (作者) Yu, Yi-Tingen_US
dc.date (日期) 2023en_US
dc.date.accessioned 6-Jul-2023 16:57:24 (UTC+8)-
dc.date.available 6-Jul-2023 16:57:24 (UTC+8)-
dc.date.issued (上傳時間) 6-Jul-2023 16:57:24 (UTC+8)-
dc.identifier (Other Identifiers) G0108752001en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145908-
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) 108752001zh_TW
dc.description.abstract (摘要) 人類是群性的物種，而語言正是群體機制運作中不可或缺的情報資訊。近年以心理建構主義（psychological constructionist）為基礎的概念行動理論（Conceptual Act Theory）強調人們對於理解語言中的情緒訊息依據可以是情緒詞彙，也可以是讀者自身攜帶情緒意涵的情節知識。心理學家一直希望從人們的話語中去了解個體的思考與情緒內容，然而，直到本世紀初拜電腦技術的進展，心理學家才開發出突破性的電腦化文本分析技術，該技術仰賴將不同詞彙歸類至數個詞類之中，並經過大規模的評定以確立它們作為文本分析效標的信效度。由於有事先定義好的詞類，因此研究者稱其為封閉式詞彙分析。封閉式詞彙分析其最大缺點在於，同一詞彙解釋方式都是固定的，並忽略了上下文脈絡。根據概念行動理論，文本分析中情緒詞類出現的頻次，在推論文本情緒價性時應能有一定程度的助益。然而，封閉式詞彙分析去除文本脈絡也可能會減損其推論文本情緒時的檢定力（power）。同時資訊科學家亦致力於讓機器具有理解人類語文的智能，受惠於大數據時代的來臨以及各類機器學習演算法的精進，資訊科學家研發的文本分析技術已經證明可透過大量文本有效抽取出其潛在特徵，這類沒有事先評定好的文本分析方式稱為開放式詞彙分析。由於不侷限於事先決定好的詞類，因此開放式詞彙分析可能會比較有機會掌握文本內上下文語義的脈絡。然而，開放式詞彙與封閉式詞彙分析的比較研究著實不多，多數情況仍是各自在各自領域內發展。本論文透過兩者的比較以此說明人類理解文本情緒意涵時，最重要的語義表徵是什麼。首先透過研究一擴大詞類範圍進行檢驗，發現蒐集更多可能參與表達情緒的詞彙有助於情緒推論的正確率，但效率不彰且提升幅度不大。顯示人類理解文本情緒意涵時的基本單元並非以詞類為基礎的語義表徵。研究二則比較兩種不同開放式詞彙分析，結果顯示人類理解文本情緒意涵時，最重要的是能夠保留上下脈絡的語義表徵。最後透過研究三對封閉式詞彙分析的改良嘗試，鞏固研究二的結果。zh_TW
dc.description.tableofcontents 摘要 2
緒論 1
封閉式詞彙分析 3
封閉式詞彙分析的可能限制：語義脈絡 4
BERT 7
LDA 9
研究目的 13
研究材料 14
學生文本 14
網路文本 15
網路短文 16
研究一：以封閉式詞彙分析進行情緒文本分析 18
封閉式詞彙分析詞典 18
研究一A：以情緒詞詞典進行情緒推論 22
研究方法 22
研究程序 22
結果 23
研究一B：擴增情緒共現詞進行情緒推論 26
研究方法 26
刺激材料 26
研究程序 30
結果 30
研究一討論 33
研究二 34
研究方法 35
刺激材料 35
研究程序 35
結果與討論 38
研究三：封閉式詞彙分析的改良嘗試 40
研究方法 41
刺激材料 41
研究程序 41
結果與討論 44
綜合討論 45
參考文獻 48
附錄一 網路短文範例 56
附錄二 不同詞彙共現性計算情緒共現詞情緒價性 57
附錄三 透過CLS預測閱讀者對文本情緒價性判斷之正確率 59
附錄四 BERTScore recall和precision計算CLIWC詞類權重 60		zh_TW
dc.format.extent 2564538 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108752001en_US
dc.subject (關鍵詞) 文本分析zh_TW
dc.subject (關鍵詞) 情緒推論zh_TW
dc.subject (關鍵詞) 開放式詞彙分析zh_TW
dc.subject (關鍵詞) 封閉式詞彙分析zh_TW
dc.title (題名) 文本情緒推論：開放式與封閉式詞彙分析zh_TW
dc.title (題名) Textual Emotion Inference: Closed- vs. Open- Vocabulary Analysesen_US
dc.type (資料類型) thesisen_US
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