摘要(英) |
Recent trends have been influenced by the progression of text mining so researches on unstructured data mining increase rapidly. There are many literatures on using text-mining techniques for politics but studies of rhetoric of leaders are still analyzed textual materials via reading by human. This paper attempts to apply text-mining techniques to research of rhetoric of the former president Ma Ying-jeou and find the relationships between rhetoric and policy, staffing assignments or attitude toward important historical events.
In this paper, a new research method to analyze and reveal hidden ideology and decision-making process behind rhetoric of the president is developed. We present here an original work that employs Word2Vec, N-gram and t-distributed stochastic neighbor embedding (t-SNE) to assist rhetoric analysis.
The results show that mining each document sorted by date on rhetoric of the president, observing variations in data calculated by text mining techniques, and then investigating the reasons why data alter on the time can find out the association between rhetoric and policy, staffing assignments or attitude toward important historical events.
Furthermore, we disclosure the mastermind behind the scenes of regional economic integration policy was Daniel Liu(劉大年)in the period from October 2013 to May 2016, Ma Ying-jeou tried hard to make his historical role as a man bringing Cross-strait peace and the attitude of the Ma administration was perfunctory toward February 28 Incident after February 2015. |
參考文獻 |
中文文獻
公眾外交協調會,2014年2月17日。馬總統蒞臨「我國加入TPP/RCEP策略規劃研習會」。取自http://www.mofa.gov.tw/News_Content.aspx?n=11C2D1C192EB605F&sms=CA4CF59DDADDE879&s=40E046E42D96D518
王鴻志,2011。從馬英九三次元旦講話看台當局兩岸政策。兩岸關係,2011年第1期,頁29-30。
林靜伶譯,1996。當代語藝觀點,Sonja K. Foss原著。臺北市:五南書局。
林顯明,2015。再解構民主進步黨ECFA政策論述:文字探勘之初探。展望與探索,第13卷,第7期,頁89-102。
江孟穎,2003。首長形象建構之研究-以台北市與高雄市政府新聞稿為例。銘傳大學傳播管理研究所碩士論文。
孫冬雪,2015。手握“九”了心更“近”了——見證“習馬會”。兩岸關係,2015年第12期,頁6-9。
孫君意,2015年12月16日。“結巴”中文分詞:做最好的 Python 中文分詞組件。取自https://github.com/fxsjy/jieba
馬英九,2015年11月7日。馬總統開場談話全文。取自http://www.mac.gov.tw/ct.asp?xItem=113314&ctNode=5650&mp=1
殷德惠,2003。語言與權力:李登輝與民粹主義之研究。國立政治大學新聞研究所碩士論文。
商西,2013年10月7日。習近平:政治問題不能一代一代傳下去。京華時報。取自http://www.chinanews.com/gn/2013/10-07/5346801.shtml
許禎元,2003。內容分析法的研究步驟與在政治學領域的應用。師大政治論叢。第1卷,第1期,頁1-29。
張道宜,2015。脈絡下的保護責任:文本探勘的再詮釋。國立政治大學外交研究所碩士論文。
曾元顯、林瑜一,2011。內容探勘技術在教育評鑑研究發展趨勢分析之應用。教育科學研究期刊,第56卷,第1期,頁129-166。
黃巧雯,2016年5月16日。如經濟部保姆 劉大年獲頒經濟專業獎章。中央通訊社。取自http://www.cna.com.tw/news/afe/201605160194-1.aspx
喻欣凱(2008)。運用支援向量機與文字探勘於股價漲跌趨勢之預測。輔仁大學資訊管理學系研究所碩士論文。
溫品竹、蔡易霖、蔡宗翰,2015。基於Word2Vec 詞向量的網路情緒文和流行音樂媒合方法之研究。The 2015 Conference on Computational Linguistics and Speech Processing,頁167-179。
劉大年,2014年2月15日。加速形成共識 區域經濟整合 兩岸非零和賽局。聯合報,A18版。
劉大年、盧鈺雯、許茵爾,2014。全球區域經濟整合與臺灣。載於陳添枝、劉大年(主編),由ECFA到TPP:臺灣區域經濟整合之路,頁5-32。臺北市:財團法人兩岸交流遠景基金會。
謝妙玲,2002。2002年台北市長選舉候選人報紙競選廣告之研究—中國時報、自由時報、聯合報的內容分析。世新大學傳播研究所碩士論文。
英文文獻
Ari-Veikko Anttiroiko, 2003. Building strong e-democracy: the role of technology in developing democracy for the information age. Communications of the ACM. Vol. 46, Issue 9, p 121-128.
Alexa Internet, 2016. Github.com Alexa Ranking. Retrieved from http://www.alexa.com/siteinfo/github.com
Gerard Salton & Michael J. McGill, 1983. Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York, NY, USA.
Harold D. Lasswell, 1941. The World Attention Survey. The Public Opinion Quarterly. Vol. 5, No. 3, p 456-462.
Julie Weeds & David Weir, 2005. Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity. Computational Linguistics. Vol. 31, No. 4, p 439-475.
Lada A. Adamic & Natalie Glance, 2005. The political blogosphere and the 2004 U.S. election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery (LinkKDD ’05). New York, NY, USA, p 36-43.
Muneeb T. H., Sunil Kumar Sahu, & Ashish Anand, 2015. Evaluating distributed word representations for capturing semantics of biomedical concepts. ACL-IJCNLP 2015, p 158-163.
Nicholas Evangelopoulos & Lucian Visinescu, 2012. Text-mining the voice of the people. Communications of the ACM. Vol. 55, Issue 2, p 62-69.
Object Management Group®, 2015, March 1. OMG Unified Modeling Language™ Version 2.5. Retrieved from http://www.omg.org/spec/UML/2.5
Omer Levy, Yoav Goldberg, & Ido Dagan, 2015. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, Vol. 3, p 211-225.
Tae Yano, William W. Cohen, & Noah A. Smith, 2009. Predicting response to political blog posts with topic models. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL ’09), Stroudsburg, PA, USA, p 477-485.
Peter Teo, 2000. Racism in the news: a Critical Discourse Analysis of news reporting in two Australian newspapers. Discourse & Society. Vol. 11 (1), p 7-49.
Tomas Mikolov, Kai Chen, Greg Corrado, & Jeffrey Dean, 2013. Efficient Estimation of Word Representations in Vector Space. International Conference on Learning Representations 2013.
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, & Jeff Dean, 2013. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (NIPS 2013).
Zhiyuan Fang, 2002. E-Government in Digital Era: Concept, Practice, and Development. International Journal of The Computer, The Internet and Management. Vol. 10, No.2, p 1-22. |