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Text Mining:
DEFINITION
»
Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output.
 
INTRODUCTION
»
Text mining, which is sometimes referred to “text analytics”, is one way to make qualitative or “unstructured” data usable by a computer. 
 
»
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 etc.,
 
» Text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modelling (i.e., learning relations between named entities).
 
» Text mining as the process or practice of examining large collections of written resources in order to generate new information, typically using specialized computer software. It is a subset of the larger field of data mining.
 
» 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.
 
» It is also known as Knowledge Discovery in Text (KDT), refers generally to the processing of extracting interesting and nontrivial information from unstructured text.
 
Block Diagram:
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Features:
  • Enterprise Business Intelligence/Data Mining, Competitive Intelligence
  • E-Discovery, Records Management
  • National Security/Intelligence
  • Scientific discovery, especially Life Sciences
  • Sentiment Analysis Tools, Listening Platforms
  • Natural Language/Semantic Toolkit or Service
  • Publishing
  • Automated ad placement
  • Search/Information Access
  • Social media monitoring
   
Real Time Examples:
» Text Mining Software and concepts are avail from many commercial and open source companies and sources.
   
Commercial:
  • AeroText
  • Angoss
  • Attensity
  • Autonomy
  • Clarabridge
  • IBM LanguageWare
   
 
Open Source:
  • QueryTermAnalyzer
  • Carrot2
  • GATE
  • OpenNLP
  • Natural Language Toolkit
  • RapidMiner
 
 
 
 
 
 
   
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