198843 VU/3 VU Data Analysis II: Text Mining

winter semester 2019/2020 | Last update: 10.02.2020 Place course on memo list
Dr. Watanabe Kohei Dr. Watanabe Kohei, +43 512 507 70114, +43 512 507 39757
198843
VU Data Analysis II: Text Mining
VU 3
5
weekly
annually
English

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.
not applicable

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.

see dates
Group 0
Date Time Location
Mon 2019-10-07
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-10-14
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-10-21
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-10-28
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-11-04
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-11-11
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-11-18
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-11-25
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2019-12-09
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2020-01-13
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2020-01-20
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free
Mon 2020-01-27
16.00 - 19.00 SR 6 (Sowi) SR 6 (Sowi) Barrier-free