Abstract

In this study we have analyzed different techniques for information retrieval in text mining. The aim of the study is to identify web text information retrieval. Text mining almost alike to analytics, which is a process of deriving high quality information from text. High quality information is typically derived in the course of the devising of patterns and trends through means such as statistical pattern learning. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, creation of coarse taxonomies, sentiment analysis, document summarization and entity relation modeling. It is used to mine hidden information from not-structured or semi-structured data. This feature is necessary because a large amount of the Web information is semi-structured due to the nested structure of HTML code, is linked and is redundant. Web content categorization with a content database is the most important tool to the efficient use of search engines. A customer requesting information on a particular subject or item would otherwise have to search through hundred of results to find the most relevant information to his query. Hundreds of results through use of mining text are reduced by this step. This eliminates the aggravation and improves the navigation of information on the Web.

Keywords:

Information retrieval , text mining, web mining , web search engine,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.