Abstract:
This paper presents on "TEXT MINING APPLICATIONS".A general definition includes all types of text processing that deals with finding ,organizing and analyzing information.A more formal definition restricts text mining to mean the creation of new information that is not obvious in a collection of documents.
This paper also presents about the IR and NLP. We will look at some applications of text mining in three areas: business, medicine and low, and society

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Text Mining

abstract

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining 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).