Topic- and Structured Topic Modeling
You can try it:
These notebooks provide an introduction to Natural Language Processing (NLP) in R with an emphasis on Topic Modeling. The notebooks touche on issues ranging from data pre-processing and import to model inference and visualization. Readers will get a first insight into the practical aspects of text mining and relevant R packages for Quantitative Analysis of Textual Data and Structural Topic Modeling. The notebooks are intended as an extension of the talk “Introduction to topic modeling research” by Cornelius Puschmann at the BIGSSS Summer Schools in Computational Social Science in Bremen. The Jupyter version of the notebook presented by Cornelius Puschmann is included.
Set up your environment
For running these notebooks on your machiene you will need to have installed the packages quanteda, stm and tidyverse. you can do this using RStudio menu, or executing the following commands in your R console
install.packages("quanteda") install.packages("stm") install.packages("tidyverse")
- Practical Introduction to Text Mining (link)
- Text Mining for Social Scientists and Digital Humanists (link)
- Learning Structural Topic Modeling (link)
- quanteda tutorials (link)
- Text Mining with R by by Kohei Watanabe and Stefan Müller (link)
Lecturer: Arnim Bleier is a postdoctoral researcher in the Department Computational Social Science at GESIS. His research interests are in the field of Natural Language Processing and Computational Social Science. In collaboration with social scientists, he develops Bayesian models for the content, structure and dynamics of social phenomena.