Definition

text mining (text analytics)

This definition is part of our Essential Guide: Analytics technologies lend enterprise content management a hand

Text mining is the analysis of data contained in natural language text. The application of text mining techniques to solve business problems is called text analytics.

Text mining can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. Mining unstructured data with natural language processing (NLP), statistical modeling and machine learning techniques can be challenging, however, because natural language text is often inconsistent. It contains ambiguities caused by inconsistent syntax and semantics, including slang, language specific to vertical industries and age groups, double entendres and sarcasm.

Text analytics software can help by transposing words and phrases in unstructured data into numerical values which can then be linked with structured data in a database and analyzed with traditional data mining techniques. With an iterative approach, an organization can successfully use text analytics to gain insight into content-specific values such as sentiment, emotion, intensity and relevance. Because text analytics technology is still considered to be an emerging technology, however, results and depth of analysis can vary wildly from vendor to vendor.

See also: e-discovery, predictive coding

This was last updated in October 2013

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why should use text mining in data warehouse?
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data warehouse is when you have big data , big could be in terms of number , space , time  and many more , and y we use  text data mining in data warehouse 
it is because text data mining is u get a pattern on basis of data and if u ll have small data one can never be accurate so for any data mining we use data warehouse else the results wont be accurate , no pattern would be made .

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