Text Mining and Text Analytics
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. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." 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, notably life-sciences research and government intelligence.
The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
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Famous quotes containing the words text and/or mining:
“I am so glad you have been able to preserve the text in all of its impurity.”
—Samuel Beckett (1906–1989)
“For every nineteenth-century middle-class family that protected its wife and child within the family circle, there was an Irish or a German girl scrubbing floors in that home, a Welsh boy mining coal to keep the home-baked goodies warm, a black girl doing the family laundry, a black mother and child picking cotton to be made into clothes for the family, and a Jewish or an Italian daughter in a sweatshop making “ladies” dresses or artificial flowers for the family to purchase.”
—Stephanie Coontz (20th century)