text mining


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text mining

Analyzing natural language in documents, email messages and other free-form text. Text mining attempts to derive meaning from the words and sentences in order to classify documents, route messages appropriately, as well as create summaries of content. For example, email coming to a support site can be analyzed to direct the message to the appropriate technician. See noisy text and data mining.
References in periodicals archive ?
After emerging big data, especially unstructured data, text mining is considered as a suitable methodology to analysis them.
Improved word sense induction helps to disambiguate terms semi-automatically, the inclusion of statistical language models boosts text mining results, and the refined statistics and search features enhance the user experience.
Data preprocessing is most concerning to Text Mining [2] which converts the textual form of data into more suitable for data mining algorithms [4].
Text mining shares many of the same objectives as data mining: to be able to use the stores of information being produced to better understand trends, discover new information, and seek better methods or services based on research, behavior, or preferences.
Text mining can be defined as the analysis of semi-structured or unstructured text data.
Text mining can help alleviate such oversights by automatically analyzing the adjuster's notes and sending alerts about opportunities for action.
This paper introduces a new text mining framework using a tree-based Linguistic Query Language, called LQL.
Web intelligence and security; advances in data and text mining techniques for detecting and preventing terrorist activities on the web.