Natural language processing (NLP) for climate research: Practical text analysis workflows 

This course introduces students to foundational Natural Language Processing (NLP) concepts and practical applications using Orange Data Mining. Students will learn text preprocessing, explore Bag of Words and embedding-based representations, and apply word clouds, sentiment analysis, and topic modelling workflows. The course includes a group project analysing a climate-related dataset, where students design workflows, perform analyses, visualise results, and present findings. By the end of the course, students will be able to apply, interpret, and critically evaluate NLP methods and select appropriate techniques for different research contexts.

General learning outcomes

• Perform and justify text preprocessing steps appropriate to different NLP tasks
• Distinguish between Bag of Words and embedding-based text representations and their impact on analysis results
• Apply and interpret basic NLP workflows in Orange Data Mining
• Evaluate and interpret sentiment analysis results obtained using different methodological approaches
• Compare traditional and transformer-based topic modelling approaches