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
 which converts the textual form of data into more suitable for data mining algorithms .
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.
can be defined as the analysis of semi-structured or unstructured text data.
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.