Text Mining | Introduction to Natural Language Processing (NLP)



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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

The Introduction to Natural Language Processing (NLP) course allows students to explore the vast expanse of textual data for research purposes. Shaped exclusively for GSERM students, the course equips participants with robust exploratory and analytical NLP methods including large language models (LLMs), prompt engineering, and vector database basics. Other NLP topics include bag-of-words analysis, sentiment analysis, document classification and clustering. Overall, the course provides a perfect springboard for qualitative researchers intending to augment their work with text data-driven conclusions. Prior exposure to programming, graduate-level statistics, and mathematical theory is advantageous, though not strictly required. The prime focus is to render the course accessible to researchers eager to enrich their examination with NLP analysis tools.