本課程旨在介紹如何運用數位化文本作為政治學研究的資料來源。學生將學習如何透過撰寫 Python 程式與其他統計軟體，進行資料的擷取、分析與建模。
本課程將介紹運用電腦化文字分析研究政治的最新發展，內容涵蓋從文本前期處理到結果詮釋的各種常見實證分析方法。修完本課後，學生將熟悉並備使用 Python 程式語言的能力。
此外，學生也將能深入理解這些工具如何促進政治學研究的產出，並能運用這些技能進行自己的研究。選修本課程無需任何程式設計的先備知識。
請注意：由於學習狀況與進度難以預先掌握，本課程大綱僅為暫定的時間參考。指定閱讀內容可能會提前或延後。此外，本課程也會不定期建議學生閱讀一些學術期刊文章，這些文章中包含本課程所介紹方法的實際應用範例。本課程將以中文授課。
This course aims to introduce how to use digital texts as a source of data for political studies. Students will learn how to extract, analyze and model data through writing Python programs and other statistical software.
This course will introduce the latest developments in using computerized text analysis and research politics, and the content covers various common evidence analysis methods from text pre-processing to result reviews. After completing this course, students will become familiar with and prepare the ability to use Python programming languages.
In addition, students will have a deep understanding of how these tools can facilitate the production of political studies and can use these skills to conduct their own research. No pre-design knowledge is required to choose this course.
Please note: Since learning status and progress are difficult to master in advance, this course is mostly a fixed time reference. Specifies that reading may be advanced or delayed. In addition, students are also recommended from time to time to read some academic journal articles, which contain examples of the actual application of the methods introduced in this course. This course will be taught in Chinese.
Summerfield, M. (2010). Programming in Python 3: A complete introduction to the Python language (2nd ed.). Addison-Wesley.
Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied text analysis with Python: Enabling language-aware data products with machine learning. O'Reilly Media.
Summerfield, M. (2010). Programming in Python 3: A complete introduction to the Python language (2nd ed.). Addison-Wesley.
Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied text analysis with Python: Enabling language-aware data products with machine learning. O'Reilly Media.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 隨堂測驗隨堂測驗 Lutang Test |
30 | 共三次隨堂測驗，各佔學期總成績10% |
| 期中報告期中報告 Midterm Report |
30 | 書面及口頭報告各佔15% |
| 期末研究報告期末研究報告 Final research report |
40 | 口頭報告 15%，書面報告 25%。 |
