Text Mining

Text mining is an exciting area of computer science research that deals with the machine-supported analysis of text. It tries to solve the crisis of information overload by combining different techniques (data mining, machine learning, natural language processing, information retrieval and knowledge management). It provides a rapidly evolving approach to the analysis of text that shares and builds on many of the key elements of text mining. It also provides new tools for people to better leverage their burgeoning textual data resources. When we try to define text mining we have to refer to similar research areas. In these areas we can establish different definitions of text mining. One approach believes that text mining means information extraction. It can also be seen as data mining, the use of algorithms and methods from the fields machine learning and statistics to texts with the goal of finding useful patterns. We frequently find in literature text mining as a process with a series of partial steps, among other things also information extraction as well as the use of data mining or statistical procedures. We can summarize this in a general manner as the extraction of not yet discovered information in large collections of texts.

Feldmann, Ronen/Sanger, James. The text mining handbook: advanced approaches in analysing unstructured data, Cambridge University Press, 2007.

Hotho, Andreas/Nürnberger, Andreas, A Brief Survey of Text Mining. In: Ldv Forum Vol 20 (2005)

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