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This thesis conducts a text and network analysis of criminological files. The specific focus during the research is the field money laundering. The analysis showed the most important concepts present in the text which were classified in eleven different classes. The relationships of those concepts were analysed using ego networks, key entity identification and clustering. Some of the statements given about money laundering could be validated by the findings of this analysis and their interpretation. Specific concepts like banks and organizations as well as foreign subsidiaries were identified. Aggregating these concepts with the statements in chapter 1.4.3 on the circular process of money laundering it can be stated that different organizations and individuals, present in the criminological files, were placing money through different banks, organizations and investments in the legal financial market. At last this thesis tries to validate the benefits of the used tools for the kind of conducted research process. An estimation on ORA's and Automap's applicability for this kind of research is given in the end.
In this work a framework is developed that is used to create an evaluation scheme for the evaluation of text processing tools. The evaluation scheme is developed using a model-dependent software evaluation approach and the focus of the model-dependent part is the text-processing process which is derived from the Conceptual Analysis Process developed in the GLODERS project. As input data a German court document is used containing two incidents of extortion racketeering which happened in 2011 and 2012. The evaluation of six different tools shows that one tool offers great results for the given dataset when it is compared to manual results. It is able to identify and visualize relations between concepts without any additional manual work. Other tools also offer good results with minor drawbacks. The biggest drawback for some tools is the unavailability of models for the German language. They can perform automated tasks only on English documents. Nonetheless some tools can be enhanced by self-written code which allows users with development experience to apply additional methods.