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研究生:陳弘倫
研究生(外文):CHEN,HUNG-LUN
論文名稱:網民輿論與公共議題之情感語意分析—以2022年兵役延長議題為例
論文名稱(外文):A Semantic Analysis of Public Opinions in Online Discussion Forums:Take the 2022 Military Service Extension Discussion as an Example
指導教授:曾淑芬曾淑芬引用關係
指導教授(外文):TSENG,SHU-FEN
口試委員:陳志成李怡慧
口試委員(外文):CHEN,CHIH-CHENGLEE,YI-HUI
口試日期:2024-06-11
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:49
中文關鍵詞:社群輿論民意情感語意分析PTT
外文關鍵詞:Social MediaPublic OpinionSemantic AnalysisPTT
相關次數:
  • 被引用被引用:0
  • 點閱點閱:25
  • 評分評分:
  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
社群媒體已成為民眾分享資訊與討論公共議題的主要媒介,公共議題在社群媒體的討論度與影響力也隨之增長,成為政府了解民意之重要途徑。本研究蒐集社群媒體PTT八卦版2022年9月至2023年1月,網民對兵役延長議題的討論內容,共453篇文章與25,054條留言。將網民對於此議題的討論分成三個階段,「政策議題形成前期」、 「政策內容規劃中期」、和「政策議題形成後期」,分別運用情感語意分析來了解網民語意情感的變化過程。並使用 BERTopic主題模型,歸納網民在各階段所討論的主題與網民在政策形成前後立場之差異。研究結果顯示,網民的語意在多數的情況下呈現負面、激動的情感,特別是針對涉及兵役延長的時間、公平性,以及對政策的看法等。在政策議題形成前期,討論的主題圍繞「抗中保台」,網民多數表示支持政策。政策內容規劃中期的討論則集中於「立場論戰」,儘管網民存在不同立場的辯論,支持留言仍多於反對留言。政策議題形成後期,討論焦點則轉移到「發言爭議」,反諷特定政治人物的留言增加,對於兵役延長議題的支持留言減少,反對的留言增加。本研究發現網民對兵役延長的議題在不同階段的討論過程有明顯的情感和立場的轉變。這個結果顯示,社群媒體輿論在反映與影響民意上扮演著關鍵角色。
Social media has become a primary platform for sharing information and discussing public issues. The discussion and influence of public issues on social media have grown accordingly, making it an essential channel for the government to understand public opinion. This study collected and analyzed 453 posts and 25,054 comments on the topic of military service extension from the Gossip Board on PTT from September 2022 to January 2023. Three time periods were distinguished for analysis purpose: the early stage of policy formation, the mid-stage of policy planning, and the late stage of policy formation. Sentiment analyses were employed to understand the emotional changes among netizens during different time periods. Furthermore, the BERTopic model was utilized to identify themes discussed at each stage and to analyze the differences in their stances before and after the formation of the policy. The results indicated that netizens generally exhibited negative sentiments with a high level of arousal, particularly when discussions involved the duration of military service extension, fairness, and perspectives on the policy. During the early stage of policy formation, discussions primarily revolved around "resist China and protect Taiwan" with the majority of netizens supporting the policy. In the mid-stage of policy planning, discussions shifted to "stance debates," where supportive voices still outnumbered opposing ones despite differing opinions. In the late stage, the focus moved to "speech controversy," marked by an increase in sarcasm comments and opposing comments, while supportive comments declined. This study demonstrates that netizens' reactions to the process of policy formation, showing significant emotional and stance shifts throughout the discussion of three stages. The findings highlight the pivotal role of social media in reflecting and influencing public opinion.
書名頁 i
論文口試委員審定書 ii
中文摘要 iii
英文摘要 iv
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
第一節、研究背景與動機 1
第二節、研究目的 3
第二章 文獻回顧 4
第一節、網路輿論與社群媒體 4
一、輿論介紹 4
二、社群媒體介紹 7
三、社群媒體網路輿論之研究應用 8
第二節、社群媒體輿論與公共政策 10
一、社群媒體輿論對於公共政策形成過程的關係 10
二、社群媒體輿論對於公共政策形成之研究應用 12
第三節、社群媒體情感分析 15
一、情感分析之應用 15
二、情感分析之方法 17
第三章 研究方法 19
第一節、研究流程 19
第二節、資料來源 20
一、研究對象:PTT八卦板 20
二、資料欄位說明 22
第三節、資料分析工具 23
一、CKIP中文斷詞 23
二、BERTopic主題模型 23
三、情感分析 24
第四章 研究分析與結果 25
第一節、描述性分析 25
一、文章與留言資料 25
二、主題 32
第五章 結論與建議 42
第一節、結論與討論 42
一、研究結果 42
二、理論回應與討論 43
第二節、研究限制與未來展望 44
參考文獻 45

中文文獻
1.王蘭成(2016),鄉民到底在想什麼? 網路輿情分析術,臺北:基峰資訊股份有限公司,第1-4頁。
2.朱斌妤、黃東益、李仲彬、黃婉玲、洪永泰(2016),數位國家治理 (3):國情分析架構與方法。國家發展委員會委託研究報告(編號:NDC-MIS-104-001),臺北市:國家發展委員會。
3.朱斌妤、黃東益、洪永泰、李仲彬、曾憲立(2015),數位國家治理 (2):國情追蹤與方法整合。國家發展委員會委託研究報告(編號:NDC-MIS-103-001),臺北市:國家發展委員會。
4.吳介宇(2023),「網路輿情與議程設定:以礦業法修法歷程為例」,科際整合月刊,第八卷,第五期,第32-54頁。
5.吳定(2003),公共政策,台北:國立空中大學。
6.李永山、李家寧、黃俊閎、黃世育(2022),「應用文本分析於輿論聲量分析之研究」,資訊電子學刊,第十卷,第一期,第85-99頁。
7.李仲彬、曾憲立(2016),「公共議題的網路輿情分析-以英檢列國考資格及考試院整體議題為例」,國家菁英季刊,第十二卷,第二期,第57-75頁。
8.李忠憲(2022),運用 BERT 於稅制改革輿情分析,探討評論品質之情感分析-以房地合一稅為例,國立臺北科技大學管理學院資訊與財金管理 EMBA 專班碩士學位論文,臺北市,取自https://hdl.handle.net/11296/bnedey
9.李瞻(1992),新聞學原理. 黎明文化事業股份有限公司.
10.汪子錫(2016),「e民主時期的政治信任研究:對於臺灣2016年選舉結果的觀察與評價」,中國行政評論,第二十二卷,第三期,第38-56頁。
11.周韻采、陳俊明(2010),政府重大議題網路輿論趨勢調查研究–以死刑為例。行政院研究發展考核委員會委託研究報告(編號:0992460052),未出版。
12.林文涵(2016),網路輿情分析在公共政策的應用與影響,國立政治大學公共行政學系碩士論文,臺北市,取自https://hdl.handle.net/11296/kgnhb6。
13.林忠山(2003),「民意影響公共政策之分析:衝突及動員的面向」,華岡社科學報,第十七期,第59-83頁。
14.施伯燁(2014),「社群媒體-使用者研究之概念、方法與方法論初探」,傳播研究與實踐,第四卷第二期,第207-227頁。
15.殷志偉、劉正(2020),「非核家園的民衆意向:網路輿論的大數據分析」,選舉研究,第二十七卷,第二期,第49-92頁。
16.祝基瀅(1995),政治傳播學,台北:三民書局。
17.財團法人台灣網路資訊中心(2023 年 8 月 29 日)。2023 年台灣網路報告。台灣網路報告官網。 https://report.twnic.tw/2023/
18.曹修源、方鄒昭聰、林慶昌、吳采軒(2019),「創新的社群文字探勘方法分析2018台北市市長候選人形象定位」,Electronic Commerce Studies,第十七卷,第四期,第277-293頁。
19.陶治中、陳亭愷(2016),「社群運算應用於網路輿情情感傾向分析之研究-以實施國道計程電子收費政策為例」,運輸學刊,第二十八卷,第三期,第295-334頁。
20.黃東益、陳敦源、蕭乃沂(2006),「政策民意調查:公共政策過程中的公共諮詢」,研考雙月刊,第三十卷,第四期,第13-27頁。
21.劉嘉薇(2017),「網路統獨的聲量研究:大數據的分析」,政治科學論叢,第七十一期,第113-165頁。
22.譚偉(2003),「網絡輿論概念及特徵」,湖南社會科學,第5期,第188-190 頁。
23.蘇蘅、郭台達、潘金谷、曹嬿恆、陳棅易(2016),「2016總統大選的社群媒體、政治討論與情緒傳播:以周子瑜事件的大數據分析為例」,中華傳播學會2016年年會論文,台北市,台灣。
24.顧以謙、劉邦揚(2018),「檢察機關網路聲量與情緒分析-大數據分析」,刑事政策與犯罪防治研究專刊,第十九期,第22-36頁。

英文文獻
1.Ahmad, I. S., Bakar, A. A., & Yaakub, M. R. (2020). Movie revenue prediction based on purchase intention mining using YouTube trailer reviews. Information Processing & Management, 57(5), 102278.
2.Anderson, J. E. (2003). Public policymaking: An introduction. Cengage Learning.
3.Bae, Y., & Lee, H. (2012). Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the American society for information science and technology, 63(12), 2521-2535.
4.Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of computer‐mediated Communication, 13(1), 210-230.
5.Burstein, P. (2003). The impact of public opinion on public policy: A review and an agenda. Political research quarterly, 56(1), 29-40.
6.Calvo, R. A., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on affective computing, 1(1), 18-37.
7.Campbell, A. L., & Rigby, E. (2019). Public opinion and public policy. In New directions in public opinion (pp. 338-362). Routledge.
8.Ceron, A., & Negri, F. (2016). The “social side” of public policy: Monitoring online public opinion and its mobilization during the policy cycle. Policy & Internet, 8(2), 131-147.
9.Cortizo, J. C., Carrero, F. M., & Gómez, J. M. (2011). Introduction to the special issue: Mining social media. International Journal of Electronic Commerce, 15(3), 5-8.
10.Das, S., & Chen, M. (2001). Yahoo! for Amazon: Extracting market sentiment from stock message boards. Proceedings of the Asia Pacific finance association annual conference (APFA),
11.Denhardt, J. V., & Denhardt, R. B. (2015). The new public service: Serving, not steering. Routledge.
12.Dwianto, R. A., Nurmandi, A., & Salahudin, S. (2021). The Sentiments Analysis of Donald Trump and Jokowi's Twitters on Covid-19 Policy Dissemination. Webology, 18(1).
13.Dye, T. R. (1972). Understanding public policy. Prentice-Hall Englewood Cliffs, N.J.
14.Easton, D. (1953). The political system, an inquiry into the state of political science ([1st ] ed.). Knopf New York.
15.Grahl, T. (2013). The 6 Types of Social Media. Retrieved May 21, 2018.
16.Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013). Use of sentiment analysis for capturing patient experience from free-text comments posted online. Journal of medical Internet research, 15(11), e2721.
17.Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
18.Habermas, J. (1962). The structural transformation of the public sphere: An inquiry into a category of bourgeois society. MIT press.
19.Hopper, A. M., & Uriyo, M. (2015). Using sentiment analysis to review patient satisfaction data located on the internet. Journal of health organization and management, 29(2), 221-233.
20.Ince, J., Rojas, F., & Davis, C. A. (2017). The social media response to Black Lives Matter: How Twitter users interact with Black Lives Matter through hashtag use. Ethnic and racial studies, 40(11), 1814-1830.
21.Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
22.Keller, M. S., Park, H. J., Cunningham, M. E., Fouladian, J. E., Chen, M., & Spiegel, B. M. R. (2017). Public perceptions regarding use of virtual reality in health care: a social media content analysis using Facebook. Journal of medical Internet research, 19(12), e419.
23.Kim, D. S., & Kim, J. W. (2014). Public opinion sensing and trend analysis on social media: A study on nuclear power on Twitter. International Journal of Multimedia and Ubiquitous Engineering, 9(11), 373-384.
24.Kušen, E., & Strembeck, M. (2018). Politics, sentiments, and misinformation: An analysis of the Twitter discussion on the 2016 Austrian Presidential Elections. Online Social Networks and Media, 5, 37-50.
25.Lasswell, H. D., & Kaplan, A. (1952). Power and Society: A Framework for Political Inquiry. Science and Society, 16(4), 346-351.
26.Lee, L.-H., Li, J.-H., & Yu, L.-C. (2022). Chinese EmoBank: building valence-arousal resources for dimensional sentiment analysis. Transactions on Asian and Low-Resource Language Information Processing, 21(4), 1-18.
27.Lippmann, W. (1922). Public opinion. Routledge.
28.Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
29.Ma, W.-Y., & Chen, K.-J. (2003). Introduction to CKIP Chinese word segmentation system for the first international Chinese Word Segmentation Bakeoff Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17, Sapporo, Japan. https://doi.org/10.3115/1119250.1119276
30.Mettler, S., & Soss, J. (2004). The consequences of public policy for democratic citizenship: Bridging policy studies and mass politics. Perspectives on politics, 2(1), 55-73.
31.Neubaum, G., & Krämer, N. C. (2017). Monitoring the opinion of the crowd: Psychological mechanisms underlying public opinion perceptions on social media. Media psychology, 20(3), 502-531.
32.Ning, K.-C., & Lai, K.-c. (2010). Sentiment Analysis in Scenic Spot Experiences from Web Community Using Semantically Relevant Contextual Information [In Chinese]. ROCLING/IJCLCLP,
33.Nisbet, M. C., & Kotcher, J. E. (2009). A two-step flow of influence? Opinion-leader campaigns on climate change. Science Communication, 30(3), 328-354.
34.Page, B. I., & Shapiro, R. Y. (1983). Effects of public opinion on policy. American political science review, 77(1), 175-190.
35.Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2), 1-135.
36.Ripley, R. B., & Franklin, G. A. (1984). Congress, the Bureaucracy, and Public Policy. Dorsey Press. https://books.google.com.tw/books?id=_rmGAAAAMAAJ
37.Rousseau, J.-J. (1964). The social contract (1762). Londres.
38.Sobkowicz, P., Kaschesky, M., & Bouchard, G. (2012). Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web. Government information quarterly, 29(4), 470-479.
39.Strapparava, C., & Valitutti, A. (2004). Wordnet affect: an affective extension of wordnet. Lrec,
40.Sukma, E. A., Hidayanto, A. N., Pandesenda, A. I., Yahya, A. N., Widharto, P., & Rahardja, U. (2020). Sentiment analysis of the new indonesian government policy (omnibus law) on social media twitter. 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS),
41.Suroso, H., Budi, I., Santoso, A. B., & Putra, P. K. (2020). Sentiment Analysis on “Homecoming Tradition Restriction” Policy on Twitter. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE),
42.Tavoschi, L., Quattrone, F., D’Andrea, E., Ducange, P., Vabanesi, M., Marcelloni, F., & Lopalco, P. L. (2020). Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Human vaccines & immunotherapeutics, 16(5), 1062-1069.
43.Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American society for information science and technology, 61(12), 2544-2558.
44.Trivedi, S. K., & Singh, A. (2021). Twitter sentiment analysis of app based online food delivery companies. Global Knowledge, Memory and Communication, 70(8/9), 891-910.
45.Vinodhini, G., & Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282-292.
46.Vu, T. T., Chang, S., Ha, Q. T., & Collier, N. (2012). An experiment in integrating sentiment features for tech stock prediction in twitter. Proceedings of the workshop on information extraction and entity analytics on social media data,
47.Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. Proceedings of the ACL 2012 system demonstrations,
48.Williams, H. T., McMurray, J. R., Kurz, T., & Lambert, F. H. (2015). Network analysis reveals open forums and echo chambers in social media discussions of climate change. Global environmental change, 32, 126-138.
49.Zhang, N., Liu, R., Zhang, X.-Y., & Pang, Z.-L. (2021). The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining. Data Science and Management, 3, 22-32.


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