Utrecht, Netherlands

Data Science: Introduction to Text Mining with R

Applications of text mining are everywhere: social media, web search, advertising, emails, customer service, healthcare, marketing, etc. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with statistical learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using and interpreting text mining on data examples from humanities, social sciences, and healthcare.

Nowadays, from social sciences to humanities and healthcare, a major portion of data is inside text. However, text is considered as a kind of unstructured information, which is difficult to process automatically. Therefore, text mining can be applied to create a more structured representation of a text, making its content more accessible to researchers. Therefore, this course offers an elaborate introduction into text mining with R. The course has a strongly practical hands-on focus, and students will gain experience in using text mining on real data from for example social sciences and healthcare domains and interpreting the results. Through lectures and practicals, the students will learn the necessary skills to design, implement, and understand their own text mining pipeline. The topics in this course include regular expressions, text preprocessing, text classification and clustering, and word embedding approaches for text data.

The course deals with:

Understand and explain the fundamental approaches to text mining
Understand and apply current methods for analyzing texts
Understand how text is handled, manipulated, preprocessed and cleaned
Define a text mining pipeline given a practical data science problem
Implement generic text mining tools such as regular expression, text clustering, text classification, sentiment analysis, and word embedding
The course starts at a very basic level and builds up gradually. At the end of the course, participants will master text mining skills with R. Participants should have a basic knowledge of data science and scripting in R.

Participants are requested to bring their own laptop computer. Software will be available online

This course is part of a series of 5 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics. Please see here for more information about the full specialisation. This course can also be taken separately.

Summer School Data Science specialisation:

Data science: Statistical Programming with R (S24)
Data science: Multiple Imputation in Practice (S28)
Data science: Introduction to Text Mining with R
Data science: Data analysis (S31)
Data science: Applied Text Mining (S42)
Upon completing 3 out of 5 courses in the specialisation (no more than one text mining course), students can obtain a certificate. Each course may also be taken separately.

Course leader

Dr. Ayoub Bagheri

Target group

This course is for R users who are interested in practical natural language processing and statistical learning on text data. Participants should have a basic knowledge of scripting and programming in R. Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences, will benefit from the course.

A maximum of 80 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.

Course aim

The course teaches students the necessary skills to understand how basic text mining techniques work, and how to use R for a variety of text analysis in many domains of science.

The skills addressed in this course are:

Text mining definitions;
R environment;
Regular expressions;
Preprocessing text data;
Stemming;
Text visualisation;
Text classification;
K-fold cross validation;
Sentiment analysis;
Text clustering;
Word embedding.

Fee info

EUR 730: Course + course materials
EUR 250: Housing fee (optional)