Introduction to Machine Learning for Text Analysis with Python

The course will provide insights into the concepts, challenges, and opportunities associated with data so large that traditional research methods (like manual content analysis) cannot be applied anymore and traditional inferential statistics start to lose their meaning. Participants are introduced to strategies and techniques for capturing and analyzing digital data in communication contexts using Python. The course offers hands-on instructions regarding the several stages of computer-aided content analysis. More, in particular, students will be familiarized with pre-processing methods, analysis strategies, and the visualization and presentation of findings. The focus will be in particular on machine learning techniques to analyze quantitative textual data, amongst which both deductive (e.g., supervised machine learning and inductive (e.g., unsupervised machine learning) approaches will be discussed.
This is a beginner's course. Participants who are looking to learn about the latest developments in machine learning for textual data (such as transformer models) should consider taking a different course. These techniques will be (briefly) discussed towards the end of the course, but the focus lies on the basics of natural language processing and classical machine learning in Python.

Course leader

Prof. Dr. Damian Trilling, Prof. Dr. Anne Kroon

Target group

Participants will find the course useful if:
- They are social scientists who have the ambition to model quantitative textual data. Specifically, those who aim to describe, explain or predict the content of large-scale textual data using computation techniques are likely to benefit from participating in this course.
- Note that non-textual data, such as images or networks, are not at the center of this course. The techniques we cover are partly generalizable to such types of data but note that the course is not tailored towards them.

Course aim

By the end of the course participants will:
- be able to identify research methods from computer science and computational linguistics which can be used for research in the domain of social science;
- understanding of the principles of supervised and unsupervised machine learning;
- be able to explain the principles of these methods and describe the value of these methods;
know to analyze textual data;
- have basic knowledge of the programming language Python and know how to use Python modules for questions relevant in the domain of the social sciences;
- be able to independently analyze quantitative textual data using machine learning techniques.

Fee info

EUR 500: Students
EUR 750: Academics

Scholarships

None