Text mining

Text mining, sometimes alternately referred to as text data mining, refers generally to the process of deriving high quality information from text. High quality information is typically derived through the dividing of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

“Text mining” is strongly related to:

Sentiment analysis Data mining Attensity Nature Genetics RapidMiner Concept mining Document classification AeroText SPSS Information retrieval Computational linguistics Database BioCreative W-shingling Thomson Data Analyzer Document clustering Endeca Technologies Inc. National Centre for Text Mining IBM PubGene UIMA Expert System S.p.A. Pervasive Data Integrator Inxight Languageware Customer attrition University of California, Berkeley School of Information Text analytics Name resolution General Architecture for Text Engineering Automatic summarization Non-negative matrix factorization Microsoft Relevance (information retrieval) PLoS Computational Biology Web mining Stop words Fair Isaac Named entity recognition Nucleic Acids Research

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Text mining

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