Text Mining

Text mining is yet another vital method in the interpretation of unstructured information. It varies from the typical web search, where users are able to discover previously written documents. In this system, text mining digitally extracts data from various resources and links them together to present new records that were otherwise unknown.

If data mining deals largely with finding patterns in structured databases, text mining is involved strictly with information extraction from text documents. The process involved requires parsing the text, deriving patterns found in the document, and finally analyzing and interpreting the relationships involved. A combination of significance and uniqueness in the files will result in high- quality conclusions.

A large concern with text mining is that it has certain limitations. Since this process involves natural languages, information recorded in non-textual form are immediately excluded from this technique. Knowledge gathered from audio-visual media and podcasts or even unpublished records, for instance, will not be included in the predictive report. Despite this drawback, text mining still proves to be a vital method in the management of unstructured information. Various tools and software have already been developed, and this technology is still being honed to enable a more accurate mining and analysis process to yield more sophisticated results.