Text Mining the Great Unread: Data-intensive methods and digital tools for analysis of texts in the humanities and social sciences

TEXT MINING THE GREAT UNREAD: DATA-INTENSIVE METHODS AND DIGITAL TOOLS FOR ANALYSIS OF TEXTS IN THE HUMANITIES AND SOCIAL SCIENCES

Books, documents, and newspapers have always been essential information resources in the humanities and social sciences. With the arrival of digital media the amount of texts available is increasing at an extremely high velocity. Classical methods for text analysis are defeated by the sheer volume leading to a massive amount of unread documents. To handle this ‘great unread’ we have to invent new computer-assisted ways of discovering meaningful patterns in texts. Such digital tools and data-intensive methods for handling big text-heavy data are currently transforming our research disciplines as well as the society around us.

 

In Text Mining the Great Unread you will learn how to develop and apply tools and methods for mining high quality information in texts. This will enable you to answer text-related research questions from the humanities and social sciences in a highly innovative and efficient manner. You will learn how to access digital databases, analyze thousands of documents and communicate your results to academia and industry alike.

 

The aim of the course is to provide theoretical, methodological and practical competences in text mining for humanities and social science students. The course will cover 1: how to delineate and evaluate research problems in terms of text mining solutions; 2: how to design and implement knowledge discovery pipelines; and 3: how to communicate through presentation, visualization and reports.

 

The core of the course is a series of hands-on workshops that cover concrete text-related problems and solutions. Workshops are supplemented by lectures and tutorials given by international researchers and industry experts. Participants will have the option of working with their own data sets and corpora. Importantly, participants are not expected to have prior experience with text mining (i.e., programming, statistics, or visualization). The only prerequisite is a positive interest in discovering new knowledge in texts.

Course description

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This course is a part of 2017 Academy.

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