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					Text Mining
Recently, text mining has received attention in many areas.
Security applications
One of the largest text mining applications that exists is probably the classified
ECHELON surveillance system. Additionally, many text mining software
packages such as AeroText, Attensity, SPSS and Expert System are
marketed towards security applications, particularly analysis of plain text
sources such as Internet news.
In 2007, Europol's Serious Crime division developed an analysis system in
order to track transnational organized crime. This Overall Analysis System for
Intelligence Support (OASIS) integrates among the most advanced text
analytics and text mining technologies available on today's market. This
system led Europol to make the most significant progress to support law
enforcement objectives at the international level. Biomedical applications
Biomedical text mining
A range of text mining applications in the biomedical literature has been
described. One example is PubGene that combines biomedical text mining
with network visualization as an Internet service. Another example, which
uses ontologies with textmining is GoPubMed.org.
Software and applications
Research and development departments of major companies, including IBM
and Microsoft, are researching text mining techniques and developing
programs to further automate the mining and analysis processes. Text mining
software is also being researched by different companies working in the area
of search and indexing in general as a way to improve their results.
Online Media applications
Text mining is being used by large media companies to disambiguate
information and to provide readers with greater search experiences, which in
turn increases site "stickiness" and revenue. Additionally, on the back end,
editors are benefiting by being able to share, associate and package news
across properties, significantly increasing opportunities to monetize content.
Marketing applications
Text mining is starting to be used in marketing as well, more specifically in
analytical Customer relationship management. Coussement and Van den
Poel (2008) apply it to improve predictive analytics models for customer churn
(Customer attrition).
Academic applications
The issue of text mining is of importance to publishers who hold large
databases of information requiring indexing for retrieval. This is particularly
true in scientific disciplines, in which highly specific information is often
contained within written text. Therefore, initiatives have been taken such as
Nature's proposal for an Open Text Mining Interface (OTMI) and NIH's
common Journal Publishing Document Type Definition (DTD) that would
provide semantic cues to machines to answer specific queries contained
within text without removing publisher barriers to public access.
Academic institutions have also become involved in the text mining initiative:
The National Centre for Text Mining, a collaborative effort between the
Universities of Manchester and Liverpool, provides customised tools, research
facilities and offers advice to the academic community. They are funded by
the Joint Information Systems Committee (JISC) and two of the UK Research
Councils. With an initial focus on text mining in the biological and biomedical
sciences, research has since expanded into the areas of Social Science.
In the United States, the School of Information at University of California,
Berkeley is developing a program called BioText to assist bioscience
researchers in text mining and analysis.
Implications
Until recently websites most often used text-based lexical searches; in other
words, users could find documents only by the words that happened to occur
in the documents. Text mining may allow searches to be directly answered by
the semantic web; users may be able to search for content based on its
meaning and context, rather than just by a specific word.
Additionally, text mining software can be used to build large dossiers of
information about specific people and events. For example, by using software
that extracts specifics facts about businesses and individuals from news
reports, large datasets can be built to facilitate social networks analysis or
counter-intelligence. In effect, the text mining software may act in a capacity
similar to an intelligence analyst or research librarian, albeit with a more
limited scope of analysis.
Text mining is also used in some email spam filters as a way of determining
the characteristics of messages that are likely to be advertisements or other
unwanted material.

				
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posted:10/4/2011
language:English
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