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題名 COVID-19期間對非援助網絡輿情分析:以Twitter為研究標的
Analysis of Public Opinion of the Aid to Africa During COVID-19 Era on Twitter作者 陳妍如
Chen, Yen-Ju貢獻者 李明
Lee, Ming
陳妍如
Chen, Yen-Ju關鍵詞 對非援助
新古典現實主義
民意
R語言
文字探勘
情感分析
Aid to African
Neoliberalism
public opinion
R programming
text-mining
sentimental analysis日期 2021 上傳時間 10-二月-2022 13:16:46 (UTC+8) 摘要 本研究使用R語言撈取Twitter平台上3000筆關於對非援助相關推文,使用情感分析、主題模型分析以及論述分析的方式,一步步剖析探討疫情期間網絡民意在對非援助議題上,所關注的方向及對非援助的成效和立場,並和文獻回顧結果做比較,以完善對非援助議題的研究。研究成果分成技術上及文字探勘兩層面來探討,在技術層次上,本研究發現情感分析及主題模型分析在對非援助議題的局限性,在文字探勘的部分,發現學者和網絡民意在對非援助議題上的見解相似,但網絡民意又多了非洲地區的聲量,這部分的意見不但可作為援助系統調整的參考方向,也是學術上可以深入探討的地方。社群媒體的發達以及文字探勘技術的進步,亦使得援助國更可取得在地聲音,援助國不應忽視這些聲量,否則會落入投入許多資源、精力,但援助行為非但無法達到促進非洲地區國家發展的良善目的,反而引來接受國的反彈及不諒解。
This study utilizes the “R language” mining tweets regarding aid to Africa on Twitter, using the sentimental, model and discourse analysis to analyze public opinion towards aid issues during the COVID-19 era. The purpose of the study is to find out the aspects that netizens would discuss when they mention aid to African as well as their thoughts regarding the effects of it.The results could be divided into two areas, one is how technology applies to the topic and the other one is the finding of text mining. First, the study found out that sentimental analysis and model analysis on the aid issue have their limitations, and discourse analysis could improve it. On the other hand, the scholars and opinions on Twitter have similar views on problems of aid to Africa. Furthermore, the public opinion on the Internet could let voices from Africa be heard by the world. Opinions could not only be referred to the donor countries for their aid policies, but also be used for further academic research.The development of social media and text mining let the donors get access to more voices from African local people. It is better for the donors to take it seriously, or they would end up involving a lot of resources and energy but receiving rebound and mistrust from recipients.參考文獻 中文部分一、 專書陳敦源、廖洲棚、黃心怡,〈政府公共溝通: 新型態網路參與及溝通策略〉,台北市:數位治理研究中心,2017。國家發展委員會,〈Web 2.0 時代的民意探勘:政府部門網路輿情分析的概念與實務〉,台北市:國家發展委員會,2017。二、 期刊王貿,〈公務人員關注議題之文字探勘:以PTT公職板為例〉,《調查研究-方法與應用》,第45期,2020,頁119-154。李問,〈亞洲「假新聞」輿論戰-及民主社會的防衛戰略〉,《新社會政策》,第54期,2017,頁15-19。何吉森,〈假新聞之監理與治理探討〉,《傳播研究與實踐》,第8卷,第2期,2018,頁1-41。林照真,〈假新聞情境初探:以阿拉伯世界的資訊逆流為例〉,《傳播研究與實踐》,第8卷,第1期,2018,頁 1-26。余采蓓、施俊名,〈運用文字探勘技術探討性相關議題之研究-以PTT論壇feminine_sex板為例〉,《性學研究》。第9.2期,2019,頁63-89。袁得嵛、章逸釩、高見、孫海春,〈基於用戶特徵提取的新浪微博異常用戶檢測方法〉,《計算機科學》,第6A卷,第47期,2020,頁364-368。黃詩淳、邵軒磊,〈以人工智慧讀取親權酌定裁判文本:自然語言與文字探勘之實踐〉,《臺大法學論叢》,第1卷,第49期,2020,頁195-224。陳世榮,〈社會科學研究中的文字探勘應用:以文意為基礎的文件分類及其問題〉,《人文及社會科學集刊》,第4卷,第27期,2015,頁683-718。謝吉隆、楊苾淳,〈從「應變自然」到「社會應變」:以文字探勘方法檢視國內風災新聞的報導演變〉,《教育資料與圖書館學》,第55卷,第3期,2018,頁285-318。鄭宇君、陳恭、陳百齡,〈社群媒體巨量資料搜集與分析-以Facebook與Twitter為例〉,《人文與社會科學簡訊》,第19卷,第1期,2017,頁37-46。蕭乃沂、朱斌妤,〈資料驅動創新的跨域公共治理〉,《國土及公共治理季刊》,第6卷,第4期,2018,頁74-85。三、 學位論文郝志揚,〈使用文字探勘實作新聞事件追蹤〉,《淡江大學資訊工程學系碩士班學位論文》,2017,頁1-58。四、 網路資料世界銀行,〈14張圖回顧2019年〉,2019,網址:https://www.shihang.org/zh/news/feature/2019/12/20/year-in-review-2019-in-charts(檢閱日期:2021年01月06日)。Weka簡介與實作:資料探勘的分群、異常偵測、關聯規則探勘、分類,網址:http://blog.pulipuli.info/2019/10/weka-practice-data-mining-with-weka.html#(檢閱日期:2021年04月05日)。發掘文件中的主題:Weka分群應用於文本探勘,網址:http://blog.pulipuli.info/2019/07/weka-discover-topic-of-text-collection.html(檢閱日期:2021年04月08日)。分群與分類的整合應用:無監督分類器,網址:http://blog.pulipuli.info/2017/10/building-unsupervised-classification.html(檢閱日期:2021年04月10日)。簡單易用的中英文斷詞和詞性標註:Python-Jieba ,網址:http://blog.pulipuli.info/2019/01/python-jieba-word-segmentation-and-part.html(檢閱日期:2021年04月22日)。英文部分一、 官方文件OECD. “Aid by DAC Members Increases in 2019 with More Aid to the Poorest Countries.” 2020.OECD. 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國立政治大學
外交學系
108253009資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108253009 資料類型 thesis dc.contributor.advisor 李明 zh_TW dc.contributor.advisor Lee, Ming en_US dc.contributor.author (作者) 陳妍如 zh_TW dc.contributor.author (作者) Chen, Yen-Ju en_US dc.creator (作者) 陳妍如 zh_TW dc.creator (作者) Chen, Yen-Ju en_US dc.date (日期) 2021 en_US dc.date.accessioned 10-二月-2022 13:16:46 (UTC+8) - dc.date.available 10-二月-2022 13:16:46 (UTC+8) - dc.date.issued (上傳時間) 10-二月-2022 13:16:46 (UTC+8) - dc.identifier (其他 識別碼) G0108253009 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138982 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 外交學系 zh_TW dc.description (描述) 108253009 zh_TW dc.description.abstract (摘要) 本研究使用R語言撈取Twitter平台上3000筆關於對非援助相關推文,使用情感分析、主題模型分析以及論述分析的方式,一步步剖析探討疫情期間網絡民意在對非援助議題上,所關注的方向及對非援助的成效和立場,並和文獻回顧結果做比較,以完善對非援助議題的研究。研究成果分成技術上及文字探勘兩層面來探討,在技術層次上,本研究發現情感分析及主題模型分析在對非援助議題的局限性,在文字探勘的部分,發現學者和網絡民意在對非援助議題上的見解相似,但網絡民意又多了非洲地區的聲量,這部分的意見不但可作為援助系統調整的參考方向,也是學術上可以深入探討的地方。社群媒體的發達以及文字探勘技術的進步,亦使得援助國更可取得在地聲音,援助國不應忽視這些聲量,否則會落入投入許多資源、精力,但援助行為非但無法達到促進非洲地區國家發展的良善目的,反而引來接受國的反彈及不諒解。 zh_TW dc.description.abstract (摘要) This study utilizes the “R language” mining tweets regarding aid to Africa on Twitter, using the sentimental, model and discourse analysis to analyze public opinion towards aid issues during the COVID-19 era. The purpose of the study is to find out the aspects that netizens would discuss when they mention aid to African as well as their thoughts regarding the effects of it.The results could be divided into two areas, one is how technology applies to the topic and the other one is the finding of text mining. First, the study found out that sentimental analysis and model analysis on the aid issue have their limitations, and discourse analysis could improve it. On the other hand, the scholars and opinions on Twitter have similar views on problems of aid to Africa. Furthermore, the public opinion on the Internet could let voices from Africa be heard by the world. Opinions could not only be referred to the donor countries for their aid policies, but also be used for further academic research.The development of social media and text mining let the donors get access to more voices from African local people. It is better for the donors to take it seriously, or they would end up involving a lot of resources and energy but receiving rebound and mistrust from recipients. en_US dc.description.tableofcontents 目次第壹章 緒論 1第一節 研究動機與目的 1第二節 研究方法與途徑 5第三節 章節鋪陳 7第貳章 文獻回顧 9第一節 新古典現實主義與國內層次探討 9第二節 對非援助學者見解 14第三節 援助相關民意立場論述 17第參章 輿情資料撈取及研究設計 23第一節 輿情分析方法及技術探討 23第二節 對非援助輿情資料撈取及清洗 26第三節 研究流程與設計 28第肆章 輿情資料分析與詮釋 35第一節 對非援助主題模型分析 35第二節 對非援助論述分析與詮釋 45第三節 對非援助小結 57第伍章 結論 61第一節 總結 61第二節 研究限制 62第三節 政策建議暨未來研究方向 62參考文獻 65附錄-主題模型中該分類權重較高推文列舉 75 zh_TW dc.format.extent 2253753 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108253009 en_US dc.subject (關鍵詞) 對非援助 zh_TW dc.subject (關鍵詞) 新古典現實主義 zh_TW dc.subject (關鍵詞) 民意 zh_TW dc.subject (關鍵詞) R語言 zh_TW dc.subject (關鍵詞) 文字探勘 zh_TW dc.subject (關鍵詞) 情感分析 zh_TW dc.subject (關鍵詞) Aid to African en_US dc.subject (關鍵詞) Neoliberalism en_US dc.subject (關鍵詞) public opinion en_US dc.subject (關鍵詞) R programming en_US dc.subject (關鍵詞) text-mining en_US dc.subject (關鍵詞) sentimental analysis en_US dc.title (題名) COVID-19期間對非援助網絡輿情分析:以Twitter為研究標的 zh_TW dc.title (題名) Analysis of Public Opinion of the Aid to Africa During COVID-19 Era on Twitter en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部分一、 專書陳敦源、廖洲棚、黃心怡,〈政府公共溝通: 新型態網路參與及溝通策略〉,台北市:數位治理研究中心,2017。國家發展委員會,〈Web 2.0 時代的民意探勘:政府部門網路輿情分析的概念與實務〉,台北市:國家發展委員會,2017。二、 期刊王貿,〈公務人員關注議題之文字探勘:以PTT公職板為例〉,《調查研究-方法與應用》,第45期,2020,頁119-154。李問,〈亞洲「假新聞」輿論戰-及民主社會的防衛戰略〉,《新社會政策》,第54期,2017,頁15-19。何吉森,〈假新聞之監理與治理探討〉,《傳播研究與實踐》,第8卷,第2期,2018,頁1-41。林照真,〈假新聞情境初探:以阿拉伯世界的資訊逆流為例〉,《傳播研究與實踐》,第8卷,第1期,2018,頁 1-26。余采蓓、施俊名,〈運用文字探勘技術探討性相關議題之研究-以PTT論壇feminine_sex板為例〉,《性學研究》。第9.2期,2019,頁63-89。袁得嵛、章逸釩、高見、孫海春,〈基於用戶特徵提取的新浪微博異常用戶檢測方法〉,《計算機科學》,第6A卷,第47期,2020,頁364-368。黃詩淳、邵軒磊,〈以人工智慧讀取親權酌定裁判文本:自然語言與文字探勘之實踐〉,《臺大法學論叢》,第1卷,第49期,2020,頁195-224。陳世榮,〈社會科學研究中的文字探勘應用:以文意為基礎的文件分類及其問題〉,《人文及社會科學集刊》,第4卷,第27期,2015,頁683-718。謝吉隆、楊苾淳,〈從「應變自然」到「社會應變」:以文字探勘方法檢視國內風災新聞的報導演變〉,《教育資料與圖書館學》,第55卷,第3期,2018,頁285-318。鄭宇君、陳恭、陳百齡,〈社群媒體巨量資料搜集與分析-以Facebook與Twitter為例〉,《人文與社會科學簡訊》,第19卷,第1期,2017,頁37-46。蕭乃沂、朱斌妤,〈資料驅動創新的跨域公共治理〉,《國土及公共治理季刊》,第6卷,第4期,2018,頁74-85。三、 學位論文郝志揚,〈使用文字探勘實作新聞事件追蹤〉,《淡江大學資訊工程學系碩士班學位論文》,2017,頁1-58。四、 網路資料世界銀行,〈14張圖回顧2019年〉,2019,網址:https://www.shihang.org/zh/news/feature/2019/12/20/year-in-review-2019-in-charts(檢閱日期:2021年01月06日)。Weka簡介與實作:資料探勘的分群、異常偵測、關聯規則探勘、分類,網址:http://blog.pulipuli.info/2019/10/weka-practice-data-mining-with-weka.html#(檢閱日期:2021年04月05日)。發掘文件中的主題:Weka分群應用於文本探勘,網址:http://blog.pulipuli.info/2019/07/weka-discover-topic-of-text-collection.html(檢閱日期:2021年04月08日)。分群與分類的整合應用:無監督分類器,網址:http://blog.pulipuli.info/2017/10/building-unsupervised-classification.html(檢閱日期:2021年04月10日)。簡單易用的中英文斷詞和詞性標註:Python-Jieba ,網址:http://blog.pulipuli.info/2019/01/python-jieba-word-segmentation-and-part.html(檢閱日期:2021年04月22日)。英文部分一、 官方文件OECD. “Aid by DAC Members Increases in 2019 with More Aid to the Poorest Countries.” 2020.OECD. 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