By combining Fraud Triangle Analytics with text-mining and content analytics, indication and evidence of incentive, pressure, opportunity, and rationalization, can be detected by using keywords, but also by looking for specific lexical, syntactic and semantic patterns which indicate possible fraudulent activities
Only then can organizations leverage their knowledge assets to support search, litigation, eDiscovery, text mining, sentiment analysis, and business intelligence
I also have the pleasure of holding the extra-ordinary Chair in Text Mining from the Department of Knowledge Engineering at the University of Maastricht and am also a senior research fellow of the Dutch School for Information and Knowledge Systems (SIKS)
Now, this is where text-mining and content analytics become especially valuable: ( http://zylab.wordpress.com/2010/01/26/finding-relevant-information-without-knowing-exactly-what-is-available-or-what-you-are-looking-for/ )
Being able to search within such vast data populations required significant R&D , and that type of R&D investment will continue as there are no signs that the data growth will stop (http://zylab.wordpress.com/2010/07/02/to-infinity-and-beyond-how-to-avoid-ediscovery-3d-2/). 2. Text mining (both statistical and linguistic) and other exploratory search types such as faceted search (http://zylab.wordpress.com/2010/05/28/faceted-search-how-to-go-from-a-static-to-a-dynamic-taxonomy/) have contributed significant to the usability of search interfaces. 15 years ago, there was not enough electronic data to train the statistical algorithms and there was not enough coverage of languages to implement proper disambiguation of, for instance, pronouns, co-references and entity boundaries (http://zylab.wordpress.com/2010/04/28/how-to-find-more/). 3
But, it is also possible to develop text mining and other content analytics applications with multi-linguality requirements in mind
In the last decade, content analytics have been applied with enormous success in fields such as intelligence, security and law enforcement. Sentiment mining (voice of the customer), analytics on clinical research and early detection of product guarantee problems have saved companies millions
These days, it is also possible to use text mining technology to derive location based information in textual documents with high precision
Here is why: There are special issues that one has to take into account when applying, for instance, entity-, fact-, event- and concept extraction techniques in text mining and where Natural Language Processing can make the difference: Variant Identification and Grouping: It is sometimes needed to recognize variant names as different forms of the same entity giving accurate entity counts as well as the location of all appearances of a given entity
His experience has focused on taking complex technologies, such as data mining and document management, and developing them into commercial solutions largely in the retail, web and customer relationship management (CRM) areas. -- Maria Lim Senior IM and SharePoint Consultant IM Tech Solutions Ltd --