Institution: see Organisers & Supporters
Programme of study: International Research Workshop
Lecturer: Assoc. Prof. Dr Fabian Hattke (University of Bergen)
Date: see Workshop Programme
Max. number of participants: 20
Credit Points: 5 CP for participating in the whole IRWS
Language of instruction: English
Contents: This course provides a basic introduction to the field of quantitative text analytics
and natural language processing (NLP). It offers a theoretical introduction and hands-on
exercises to explore the potential utility of different approaches to textual data (e.g., closed vs.
open vocabulary text mining, sentiment analysis, topic detection, and data visualization). The
course teaches students to extract and process text from documents and analyse the data through quantitative methods.
Software Installations: The course requires no coding or programming skills or prior
experience with NLP tools. If students want to participate in the practical exercises /actively
use their own datasets, they shall install the following software tools on their personal laptops
prior to the course.
- Linguistic Inquiry and Word Count (LIWC) https://www.liwc.app/
- Meaning Extraction Helper (MEH) https://www.ryanboyd.io/software/meh/
- A generic statistics program like Stata, SPSS, or R.
Eichstaedt, J. C., Kern, M. L., Yaden, D. B., Schwartz, H. A., Giorgi, S., Park, G., … & Ungar, L. H. (2021). Closed-and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations. Psychological Methods, 26(4), 398-427.
Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297.
Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146.
Indurkhya, N., & Damerau, F. J. (Eds.). (2010). Handbook of Natural Language Processing
(Vol. 2). CRC Press Wilkerson, J., & Casas, A. (2017). Large-scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science, 20, 529-544.
You have to register for the International Research Workshop to participate in this course.