LFU:online

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

• Technological and theoretical backgrounds of quantitative text analysis.

• Examples of quantitative text analysis in different fields, mainly in media and politics studies.

• Practical skills to manage, manipulate and analyse textual data in R.

• Basic programming skills in R.

• Working on own text analysis projects in R.

Presentation of lecture slides by lecturer; hands-on exercises using analysis examples; and presentation of exercises, and then work on final projects by participants.

Participants are graded through marks of the written exam, the homework assignments and in-class presentation of their final 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.

Participants are required to use their (laptop) computers (Windows, Linux, Mac) to the class for hands-on exercises. The course does not require good knowledge of R programming, but familiarity with general concepts of computer programing (e.g., variables, control structures) is needed. Basic knowledge of statistics (e.g., chi-square test and linear regression) is beneficial.

The acceptance procedure is based on prioritised randomisation. Students advanced in completion of the Digital Science minor get precedence. Students who completed module 1 (Introduction to Programming) and 3a (Data Analysis I) will be accepted in the first place.