Text Mining



B = Basic
M = Intermediate
A = Advanced

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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.

Text mining is the art and science of extracting insights from large amounts of natural language. The topics of Text Mining will help students add natural language processing techniques to their research, and data science toolset. As a technical course with some machine learning elements, limited exposure to programming, graduate level statistics and mathematical theory is needed but the vast majority of the course content will be focused on applying popular text mining methods. As a result, the target audience may also include qualitative researchers looking to add quantitative analysis to interviews, media and other language based field research as long as participants have some basic R background. If you stay engaged in the course and complete the suggested readings and code: Students will be able to think systematically about how information can be obtained from diverse natural language. Students will learn how to implement a variety of popular text mining algorithms in R (a free and open-source software) to identify insights, extract information and measure emotional content.