Text mining is a technique used for the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources.

However, text mining is not same as data mining. There is a significant differnce between data mining and text mining.

Text mining is a variation on a field called data mining, that tries to find interesting patterns from large databases. A typical example in data mining is using consumer purchasing patterns to predict which products to place close together on shelves, or to offer coupons for, and so on. For example, if you buy a flashlight, you are likely to buy batteries along with it. A related application is automatic detection of fraud, such as in credit card usage. Analysts look across huge numbers of credit card records to find deviations from normal spending patterns. A classic example is the use of a credit card to buy a small amount of gasoline followed by an overseas plane flight. The claim is that the first purchase tests the card to be sure it is active.

The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts. Databases are designed for programs to process automatically; text is written for people to read. We do not have programs that can "read" text and will not have such for the forseeable future. Many researchers think it will require a full simulation of how the mind works before we can write programs that read the way people do.