Quantitative Text Analytics

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 analysis 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 visualisation). The course teaches students to extract and process text from documents and to analyse the data by means of quantitative methods.

Software Installations: The course requires no coding or programming skills or prior experience with NLP tools. However, if students want to actively participate in the practical exercises and use their own datasets, they must install the following software on their personal laptops prior to the course.

  • Linguistic Inquiry and Word Count (LIWC) https://www.liwc.app/
    [the cheapest academic license is valid for 30 days and costs €18.95]
  • A generic statistics program like Stata, SPSS, or R.

Recommended literature:

  • 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.
  • 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.