| dc.contributor.advisor | 劉吉軒 | zh_TW |
| dc.contributor.advisor | Liu, Jyi Shane | en_US |
| dc.contributor.author (Authors) | 吳建良 | zh_TW |
| dc.contributor.author (Authors) | Wu, Chien Liang | en_US |
| dc.creator (作者) | 吳建良 | zh_TW |
| dc.creator (作者) | Wu, Chien Liang | en_US |
| dc.date (日期) | 2006 | en_US |
| dc.date.accessioned | 17-Sep-2009 14:02:36 (UTC+8) | - |
| dc.date.available | 17-Sep-2009 14:02:36 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Sep-2009 14:02:36 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0094753011 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32678 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 94753011 | zh_TW |
| dc.description (描述) | 95 | zh_TW |
| dc.description.abstract (摘要) | 近年來情緒逐漸在電腦科學領域中受到重視，有科學家利用人體生物感測方式來判斷情緒，再播放出適合的音樂或影片給使用者。也有用在電子寵物上，利用使用者或電子寵物的情緒來做互動。然而在語言上雖然也有對情緒作研究，大多是以人工方式對字詞做情緒上的分類，或是使用者的情緒狀態對閱讀的影響。另外我們觀察到許多的話語都含有情緒字詞或情緒意圖，而所寫的文字也同樣具有情緒。在生活中，情緒字詞往往在傳達明顯的情緒表達資訊，譬如文章中在敘述一個主角為某件事情，而所用的情緒字詞為高興，也就代表主角的情緒反應是很快樂、愉快，甚至於歡欣。因此在特定的領域或撰寫方式，大多都會透露出當時環境狀況，瞭解到當時的情緒情境資訊(Emotional Contextual Information)。情緒情境資訊的目的有三個，一為情緒詞與概念的關聯性？二為如何透過概念來喚起(Arousal)人們對某種情境所應表現出的特定情緒？三則是情緒修復(Mood Repair)，如何將人們目前所處之負向修復至正向情緒？這樣的研究能夠帶來的不只是瞭解情緒字詞與事物字詞之間的關聯，更能理解是哪些情緒來源(Source of the Emotion)會引發情緒以及相關程度，對於情意計算與相關應用上會有相當大的幫助。根據本研究目的，我們建立情緒情境共現網路，並將字詞提昇至上層概念，這部份目的在於得知何種字詞概念連結下會以那些近義詞來搭配，並簡化字詞網路的複雜度。接著則是用傳統尋找文章關鍵字方法指標，找出情緒與事物概念間的不同特性之關聯，並且我們提出新的指標來彌補傳統方法的不足。接著我們會透過控制這些指標，藉以從情境中找出哪種情緒來源可以喚起人們對情境的情緒，以及情緒修復。並經由問卷調查結果與統計分析，驗證本研究成果的確能找出與情緒情境較強關連的概念群，並藉由指標控制達到情緒喚起與修復的目的。 | zh_TW |
| dc.description.abstract (摘要) | In recent years, emotional in the computer science to be more important, some scientists have used the way of human biological sensor to recognize emotional, and then broadcast music or films for the users. Also useful in the electronic pets, using the emotion of user or electronic pets to do interaction. However, there is also research for emotional in language, but mostly based on classify the word to right emotion category by artificial way or user`s emotional state for the impact of reading. In addition, we observed speaking contain emotional word or intent, and also written. In life, emotional words often convey clear emotional expression information. For example, the article described a protagonist is happy for something, and the author use the word "happy", means that he`s emotional reaction was very happy, pleasant, and even joy. So, in specific area or writing, most of the time will reveal the state of the environment, to understand the "Emotional Contextual Information".The purposes of the emotional contextual information have three: first, the relative of emotional word and concept. Second, how to arouse the specific emotion for a situation how feeling by people. Third, Emotion repair, how to repair the emotion from negative to positive. This research is not only knowing the relative emotional words and concept, but also understanding what the source of the emotion that will be aroused. There will be a help in Affect Computing and related applications.According to the study, we have established a Emotional Situation of Co-occurrence network, and upper the word to concept. The purpose of this part is to know what the concept connection will link the synonym word in different situation, and also can simplify the complexity of network. Then using traditional indicators to find the keyword of articles, the relate with emotional word and concept. We have proposed new indicator to compensate for the drawback of traditional methods. Through the control of these indicators to find out the source of the emotional which can arouse or repair the emotion in specific situation. Finally, by the result of the questionnaire and statistical analysis. Verification results of our study will certainly identify the concepts with strong link in specific emotional situation, and through the emotional control to achieve the purpose of arousal and repair. | en_US |
| dc.description.tableofcontents | 第一章 緒論 - 1 -1.1簡介 - 1 -1.2研究背景與動機 - 1 -1.3研究目的與研究方法 - 2 -1.4論文架構 - 4 -第二章 文獻探討 - 5 -2.1網路系統 - 5 -2.1.1圖形結構(Graph Structure) - 6 -2.1.2共現網路(Co-occurrence networks) - 6 -2.1.3句法網路(Syntactic networks) - 7 -2.1.4語意網路(Semantic networks) - 8 -2.1.5小世界網路(Small World networks) - 9 -2.1.6無尺度網路(Scale free networks) - 12 -2.1.7 語言網路比較 - 14 -2.3情緒 - 15 -2.3.1情緒認知(Emotion Cognitive) - 17 -2.3.2情緒喚起(Emotional Arousal) - 19 -2.3.3情緒調整(Emotional Regulation) - 20 -2.4情境 - 22 -2.5情緒辨識相關研究 - 22 -2.6小結 - 31 -第三章 研究方法 - 32 -3.1情緒情境資訊網路模型 - 32 -3.2情緒情境共現網路 - 33 -3.2.1建立共現網路模組 - 34 -3.2.2字詞概念化模組 - 38 -3.3情緒情境資訊網路 - 41 -3.3.1平均距離關係—情境中之字詞概念關鍵字目的 - 43 -3.3.2相互訊息指標—情境中之字詞概念關鍵字目的 - 45 -3.3.3關聯度定義—字詞概念在情境中之變化目的 - 47 -3.3.4合併指標 - 49 -3.3.5指標排序 - 50 -3.4情緒及概念關係模組 - 52 -3.5情緒喚起模組 - 53 -3.6情緒修復模組 - 55 -3.6.1導引情緒修復 - 56 -3.6.2導引情緒修復方法 - 57 -第四章 實驗設計與分析 - 61 -4.1實驗資料 - 61 -4.2實驗設計規劃 - 64 -4.2.1實驗量測方法 - 64 -4.2.2問卷設計 - 65 -4.4實驗結果與分析 - 71 -4.4.1統計分析 - 72 -4.4.1.1指標效益統計分析 - 74 -4.4.1.2情緒喚起統計分析 - 84 -4.4.1.3情緒修復統計分析 - 93 -4.5總結 - 100 -第五章 結論與未來方向 - 102 -5.1結論 - 102 -5.2未來研究方向 - 104 -參考文獻 - 107 -附錄A - 114 -附錄B - 115 -附錄C - 116 - | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094753011 | en_US |
| dc.subject (關鍵詞) | 情緒 | zh_TW |
| dc.subject (關鍵詞) | 情境 | zh_TW |
| dc.subject (關鍵詞) | 情緒來源 | zh_TW |
| dc.subject (關鍵詞) | 情緒喚起 | zh_TW |
| dc.subject (關鍵詞) | 情緒修復 | zh_TW |
| dc.subject (關鍵詞) | emotion | en_US |
| dc.subject (關鍵詞) | situation | en_US |
| dc.subject (關鍵詞) | source of the emotion | en_US |
| dc.subject (關鍵詞) | arousal | en_US |
| dc.subject (關鍵詞) | emotion repair | en_US |
| dc.title (題名) | 以情緒詞為基礎之情境資訊連結與觀察 | zh_TW |
| dc.title (題名) | Contextual Information Connection and Observation Based on Emotion Words | en_US |
| dc.type (資料類型) | thesis | en |
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