Text mining is the process of analyzing data to capture key concepts, themes and uncover hidden relationships and trends without prior knowledge of the precise words. It is the process of examining data to gather valuable information. It involves algorithms of data mining, machine learning, statistics, and natural processing, attempts to extract high-quality, useful information from unstructured formats. Text mining techniques, which often interchangeably used with “text analytics” are a means by which unstructured or qualitative data is processed for machine use.
Text Mining Examples
It is used to optimize day-to-day operational efficiencies as well as improve long-term strategic decisions in the automotive, healthcare, finance sector and answer business questions. Techniques like entity extraction, categorization, and sentiment analysis are used to identify patterns, insights, and trends in large volumes of unstructured data. Here are some of the fields where text mining is used:-
The biggest reason for failure in any industry is inadequate risk analysis. However, text mining helps to resolve the issue of robust risk analysis. In the finance sector, the ability to mitigate the risk that ensures complete management of large databases, able to access the right information at the right time, and links information together.
On short notice, managing large data volumes often makes finding specific information, a difficult task. A tremendous amount of information is need by professionals, with decades of research in genomics and molecular techniques.
Prevention Of Cyber Crime
Cybercrimes are increasing day by day. The criminal which becomes unidentified soon gets untraceable. There are some applications from text mining techniques, intelligence, and anti-crime applications that are keeping cybercrimes at bay.
Basics Steps To Text Mining
Text mining when coupled with data mining offers better insight. However, the user needs to have the right understanding of both, before combining data mining with text mining.
- First, identify the text for mining. They need to prepare the text for mining. Save the files to a single location, if the text data is in multiple files. If they are mining databases, determine the field containing the text.
- After that, mine the text and extract structured data and apply the text mining algorithms to the source text.
- Now the user needs to build the concept and category models for the data. Create separate categories for key concepts after identifying each of them. Quite often, the user will find that the number of concepts from the unstructured data is too many in numbers.
Finally, the user needs to analyze the structured data. They should make use of standard data mining techniques, such as clustering, classification, and predictive modeling, to discover relationships between the concepts.