Abstract
Text mining is a semi-automated process of extracting knowledge from a large amount of unstructured data. Given that the amount of unstructured data being generated and stored is increasing rapidly, the need for automated means to process it is also increasing. In this study, we present, discuss and evaluate the techniques used to perform text mining on collections of textual information. A case study is presented using text mining to identify clusters and trends of related research topics from three major journals in the management information systems field. Based on the findings of this case study, it is proposed that this type of analysis could potentially be valuable for researchers in any field.
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