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 course: Learning outcomes

In the Text Mining course, the student will acquire knowledge on the use of the core machine learning algorithms for text mining. After the completion of this course, students will be able to preprocess textual data, understand specifics of text, transform raw text to attribute-value representation and evaluate language-based models.

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.

Lesson 6: Sentiment Analysis

The sixth lecture in the Text Mining course focuses on Sentiment Analysis (or opinion mining), a task of extracting sentiment from text data. This course is divided into three parts, for each of the approaches to sentiment extraction: 

1. Lexicon-based: use scores to relate words to sentiment. This approach uses rule-based techniques that extract opinion words and classify the document by averaging the polarity of all matched terms.
2. Machine learning: There are three types of machine learning approaches: unsupervised methods, supervised methods, and deep learning methods.
3. Hybrid: this is a machine learning approach supported by lexicon data.

The lesson includes theoretical information, and is accompanied by a practical example on lexicon-based models in order to better grasp the learning material. Each chapter combines theory with practical examples for hands-on learning. The chapters are prepared by Ajda Pretnar Žagar and Blaž Zupan with the support of members of the Bioinformatics Lab at the University of Ljubljana in Slovenia.

In the videos below you'll discover more about Text mining with Orange. You'll learn about Orange components that the educators have designed for analysis of text. The series includes tutorials on loading documents, construction of word clouds, keyword extraction, visualisation of document and word maps, text-based classification, and many other topics.