198843 VU/3 VU Datenanalyse II: Text Mining

Wintersemester 2019/2020 | Stand: 21.10.2019 LV auf Merkliste setzen
Dr. Watanabe Kohei Dr. Watanabe Kohei, +43 512 507 70114, +43 512 507 39757
198843
VU Datenanalyse II: Text Mining
VU 3
5
wöch.
jährlich
Englisch

Under the successful completion of this course students understand the basics of textual data analysis. They acquired the ability to use selected methods of textual data analysis and are capable of interpreting data and present it verbally and visually.

  • Technological and theoretical backgrounds of quantitative text analysis.
  • Examples of quantitative text analysis in different fields, mainly from political science.
  • Practical skills to manage, manipulate and analyse textual data in R.
  • Basic programming skill in R

Presentation of lecture slides by lecturer; hands-on exercises using analysis examples; and presentation of exercises and mini projects by participants.

Participants are graded through marks of written exams and/or in-class presentation of their mini projects. Details will be announced in the first session.

  • Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology. Sage.
  • Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 1–31. https://doi.org/10.1093/pan/mps028
  • Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238
  • Manning, C. D., & Schütze, H. (2001). Foundations of statistical natural language processing. Cambridge (Mass.): MIT press.
  • Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, N.J.: Pearson Prentice Hall.
  • Hastie, T. J., Tibshirani, Robert J, & Friedman, Jerome H. (2013). The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer.
keine Angabe

Participants are required to bring their laptop computers (Windows, Linux, Mac) to the class for hands-on exercises. The course does not require knowledge of programming, but familiarity with R is beneficial. Basic knowledge of statistics (chi-square test and linear regression) is necessary.

siehe Termine
Gruppe 0
Datum Uhrzeit Ort
Mo 07.10.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 14.10.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 21.10.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 28.10.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 04.11.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 11.11.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 18.11.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 25.11.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 09.12.2019 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 13.01.2020 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 20.01.2020 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Mo 27.01.2020 16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrierefrei
Gruppe Anmeldefrist
198843-0 01.09.2019 00:00 - 28.09.2019 23:59
Watanabe K.