The Text Mining course is an elective course within the eXplainable Artificial Intelligence in healthcare Management (xAIM) master’s programme. As Artificial Intelligence (AI) becomes increasingly important, especially within the healthcare sector, it is becoming crucial to address the lack of digital skills training within the sector. This master’s programme seeks to address this by training qualified healthcare professionals in the field of AI and computer scientists in the field of healthcare.

Text Mining: Learning outcomes

In the Text Mining course, the student will acquire knowledge on the use of the core machine learning algorithms for text mining. With this course, students will be introduced to natural language processing, text mining, and text analysis. They will learn to accomplish various text-related data mining tasks through visual programming. After the completion, students will be able to preprocess textual data, understand specifics of text, transform raw text to attribute-value representation and evaluate language-based models. 

Lesson 8: Document Networks

This lecture in the Text Mining course focuses on Document Networks. This course dives into the popoular computational linguistic method, co-occurrence networks, which aim to discover how words co-occur across a corpus. Using practical examples and illustrated guide, this lecture showcases how co-occurrence networks can be constructed using Corpus to Network wigdet in Orange.

These lecture notes were prepared by Ajda Pretnar Žagar and Blaž Zupan with the support of members of the Bioinformatics Lab at the University of Ljubljana in Slovenia.