Text mining is similar to data mining but it is an extended form of data mining.
It leads to discovery of new knowledge from large volume of the existing unstructured data.
Text is the most accepted form of storing information; hence text mining as a tool can be very useful for any organisation to take the advantages in this competitive age.
The importance of text mining becomes even more when we find that the proportion of unstructured data is very high.
Both text mining and data mining are usually used inter changeable, but when we compare both, we find that text mining is a much more intricate task, because it comprises of text data that are naturally unstructured and vague.
Text mining is a multidisciplinary field.
Text mining involves various processes and related to text analysis, information extraction, information retrieval, clustering, categorization, visualization, database technology, machine learning and data mining.
In other words, we can say that to retrieve useful pieces of earlier unknown “gems” of information from a large text pools is termed as text mining.
This delivers scholars with the prospect to evaluate bulks of text from multiple sources without having to go through each and every line, yet “ascertain knowledge.”
Text mining is one of the most useful and important technologies being developed to report and solve the problem of information explosion by mining the relevant information