The objective of this lecture was to give a short introduction to the Digital Humanities and to describe basic assumptions and techniques of text analysis as it is done in "text mining", an area in Computer Science. Text mining is about finding patterns in and models of text corpora. It overlaps with many other areas such as corpus linguistics, and thus is being used a lot in the Digital Humanities already. In particular, we investigated some problems that we encountered during the first parts: Can we, and may we, "just take the data as they are and expect insights from them"? What problems arise? How can we engage critically and ethically with text and other data?
The main goal of this talk was to provide an introduction to what data visualization is, including an overview of the principles, methods, and technologies used for the visual representation of data. The lecture also reviewed current trends of data visualization and practical applications where the main goal is to expose patterns that ease the understanding, communication, and decision making in the specific data field. Within this context an overview of some data visualization tools was presented. The practical part of the workshop included a helicopter overview to networks visualization, an introduction to the network analysis and graphs visualization tool Gephi with a hands on example.
The workshop focused on techniques and tools (rather than on an overview of research in the area), with the aim of giving hands-on experience on a number of techniques for exploring the contents, the sentiment, and (sentential) argument structure in texts. We set this in the context of current developments in data mining. This workshop also covered an introduction to the network analysis and graphs visualization tool Gephi with a hands on example.