GESIS Training Courses

Wiss. Koordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Koordination

Noemi Hartung

Introduction to Machine Learning for Text Analysis with Python

Mannheim, B6 4-5
Course duration:
10:00-17:00 CEST
General Topics:
Course Level:
Software used:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
Additional links
Dozierende: Marieke van Hoof , Rupert Kiddle

Referenteninformationen - Marieke van Hoof

Referenteninformationen - Rupert Kiddle


The course will provide insights into the concepts, challenges and opportunities associated with data so large that traditional research methods (like manual content analysis) can no longer be applied, and traditional inferential statistics start to lose their meaning. You 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 precisely, you 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. If you are looking to learn about the latest developments in machine learning for textual data (such as transformer models) you should consider taking a different course, e.g., “From Embeddings to LLMs: Advanced Text Analysis in Python” (23-27 September). 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.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
Organizational Structure of the Course
In the morning, we will have lectures, in which we will explain the topic of the day both from a theoretical-conceptual point of view as well as from a practical point of view (i.e., walking you through code examples). We may have small in-class exercises in between, if necessary.
In the afternoon, you will work on larger exercises in which you implement the techniques we covered. We provide example datasets, but it is also possible (and encouraged) to try to apply the techniques to own datasets. You can either opt to work on your own or try to solve problems together with one or multiple classmates. Lecturers will provide feedback on the (attempted) solutions of participants, and also provide example solutions.


You will find the course useful if:
  • You are a social scientist who has 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. Techniques we cover are partly generalizable to such types of data, but note that the course is not tailored towards them. If you are interested in working with images or networks you might be interested in one of the following courses: “Automated Image and Video Data Analysis with Python” (23-27 September, online) or “Introduction to Social Network Analysis with R” (16-20 September) or “Advanced Social Network Analysis” (23-27 September).


By the end of the course you 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
  • have an 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 how 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

  • Voraussetzungen

    • Knowledge of basic statistics (linear and logistic regression)
    • Some experience with computational methods, programming in general, and/or statistical languages (but not necessarily Python) is highly recommended to participate in this course. During the first day of the course, we will discuss some fundamental aspects of coding in Python at a fast pace. In order to follow along, we recommend those who have little previous experience with computational methods or statistical languages to take part in the online blended learning course “Introduction to Computational Social Science with Python” (30 August-06 September).
    • You are expected to have a working Python environment installed (see below), and we strongly recommend that you spend a couple of hours with one of the many free online resources to familiarize yourself with the very basics of Python to have an easier start. For a basic introduction or refresher to Python programming, you may also consider taking the online workshop "Introduction to Python" that takes place from 26-29 August. .
    Software and Hardware Requirements
    You should bring your own laptop for use in the course. You need to have a current Python environment installed and need to be able to install and update packages on your own. All relatively recent versions of Python (in general, 3.8 or higher) should be fine. If you still have an older version, you may not be able to run the example code 1:1 but need to adapt it. Make sure you have recent versions of crucial packages such as pandas, numpy, scipy, scikit-learn, gensim, and keras installed. If in doubt, check how to update them. One option to achieve all of this is to simply install the newest version of the so-called Anaconda distribution, even though this is by no means necessary (in fact, both of us usually install our packages by hand instead of using Anaconda). Additionally, it is advisable to have access to Google Colab. Therefore, please ensure that you have a Google account and can execute code through Google Colab.