I saw this comment in a recent article by Seth Grimes, where he discusses the terms Text Analysis and Text Analytics.
Within the article Mr. Grimes states that text mining and text analytics are largely interchangeable terms:
“The terms “text analytics” and “text mining” are largely interchangeable. They name the same set of methods, software tools, and applications. Their distinction stems primarily from the background of the person using each — “text mining” seems most used by data miners, and “text analytics” by individuals and organizations in domains where the road to insight was paved by business intelligence tools and methods — so that the difference is largely a matter of dialect.”
I asked Linguamatics CTO, David Milward, for his thoughts:
"There is certainly overlap, but I think there are cases of analytics that would not be classed as text mining and vice versa. Text analytics tends to be more about processing a document collection as a whole, text mining traditionally has more of the needle in a haystack connotation.
"For example, word clouds might be classified as text analytics, but not text mining. Use of natural language processing (NLP) for advanced searching is not so naturally classified under text analytics.
"Something like the Linguamatics I2E text mining platform is used for many text analytics applications, but its agile nature means it is also used as an alternative to professional search tools.
"A further term is Text Data Mining. This is usually used to distinguish cases where new knowledge is being generated, rather than old knowledge being rediscovered.
"The typical case is indirect relationships: one item is associated with a second item in one document, and the second item is associated with a third in another document. This provides a possible relationship between the first item and the third item: something which may not be possible to find within any one document."