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    Title: 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
    Authors: 陳妍如
    Chen, Yen-Ju
    Contributors: 李明
    Lee, Ming
    陳妍如
    Chen, Yen-Ju
    Keywords: 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
    Date: 2021
    Issue Date: 2022-02-10 13:16:46 (UTC+8)
    Abstract: 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.
    Reference: 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
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    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。
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    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）。
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    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
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    五、 網路資料
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    Description: 碩士
    國立政治大學
    外交學系
    108253009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108253009
    Data Type: thesis
    DOI: 10.6814/NCCU202200029
    Appears in Collections:[Department of Diplomacy] Theses

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