Introduction

This post will serve as general introduction into any other post on this blog, which is dedicated to demonstration/examples/impacts of text analytics technique in certain scenarios. To gain quick insight into what text analytics is, please have a look at Wikipedia. The short definition is:

"The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.[1] The term is roughly synonymous with text mining; indeed, Prof. Ronen Feldman modified a 2000 description of "text mining"[2] in 2004 to describe "text analytics."[3] The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s,[4] notably life-sciences research and government intelligence"

(If you would like to get more detailed story, excellent one comes from Seth Grimes)

Text analytics is about employing raw computer power to process large sets (docs, records, ...) of unstructured textual data in order to mine out structural information (categories, tags, associations, etc...) that can be used in variety of ways. For example:

• Organization of huge document sets in order to achieve better retrieval capabilities.
• Pattern recognition in text records, that leads to definition of new (for example business) rules.
• ...

There are of course more situations, where it is handy to have structural representation above the huge pile of unstructured data - which will be explored in later posts. Each post will be dedicated to one demonstration, there will be no ordering sou you can pick up and jump right into those you like. The next post will demonstrate enhanced information retrieval accomplished via text analytics.