Text and Content Analysis vis-à-vis Text Mining

It is important to clarify that text analysis is more than text mining, it may or may not include "text mining", it is more to do with trying to understand how human brain analyses text and trying to automate those processes in a manner that may not involve some very complicated AI based technology. So for K-Praxis Text Analysis (a newly resurrected and re-defined category ), includes technologies, methodologies, approaches and processes that analyze text either in relationship with or or in contrast with logically formatted structured numerical information to derive actionable information intelligence that can be acted upon by real people.

As suggested earlier on K-Praxis (Automated Content Analysis: Review of Recent Trends ), text analysis is a part of other information processing category content analysis (including processes, methodologies and obviously various content analysis technologies).

We believe that the "understanding"-capabilities-type of claims about automated and intelligent information management technologies need to be taken with a huge dollop of salt. Here for instance, any "intelligent" system will more or less fail to understand that we are playing with the idiom pinch of salt by supplanting "pinch" with "dollop" even if it had a mammoth inter-connected database of idioms and phrasal verbs.

But at the same time we are more and more convinced that human-supervised approaches where the vendors (services as well solutions vendors) solve a small niche information management problem are proving to be very fruitful and useful in variety of contexts. These approaches, methodologies (and technologies built around these approaches) will by and large determine the future of information management industry as we know it now.

Sales Marketing Intelligence: Is your company looking to buy a Sales or Marketing Intelligence solution? Then its time you analyze the solution from a Text Analysis point of view. A report by K-Praxis on Sales and Marketing Intelligence provides a roadmap for integrating Text Analysis with traditional data mining. The complete report (Sales and Marketing Intelligence: The Need for Integrating Textual Analytics with Traditional Solutions) is available for purchase through InfoSphere AB .

Relevant Quote from the New York Times :

"Currently these programs are used by academic researchers and companies, but information scientists expect that to change. Lower-cost text-mining tools eventually will be offered to ordinary people who want to dig into medical or political issues using public documents. Madan Pandit, an expert in text analysis in Bangalore, India, who runs a Web site called K-Praxis (k-praxis.com), has suggested that text mining could help people make sense of voluminous documents that are already on the Web, like the 858-page report on the congressional inquiry into intelligence failures regarding the 9/11 terrorist attacks".

"There is a need to make these technologies available for publicly available information," he wrote at his site.

In most cases, text-mining software is built upon the foundations of data mining, which uses statistical analysis to pull information out of structured databases like product inventories and customer demographics. But text mining starts with information that doesn't come in neat rows and columns. It works on unstructured data - e-mail messages, news articles, internal reports, transcripts of phone calls and the like."

(Quoted from the New York Times, October 16, 2003: )