Text Analytics

Text analytics is a technique used in interpreting unstructured information. This method is a way to enrich your search among various data, in order to optimize them into classifiable information. The process in text analytics involves applying linguistic or statistical techniques in order to categorize documents, video, images, and even audio.

A key step to this method is information extraction, where pertinent entities, facts, relationships, concepts, and even opinions must be discerned from the text. From hereon, text analytics can begin to categorize, classify, and even cluster the documents according to their varying content. This speeds up the process of indexing and search. In today’s practice, text analytics makes use of natural language processing. The typical subtasks that are used involve the following:

• identification of names and entities
• recognition of noun phrases
• extraction of named relationships
• evaluation of document contents
• singling out distinct elements within a text

The technology used in text analytics creates opportunities to utilize and exploit data found in a company’s network. This further develops the business intelligence methods, and meets the challenges that unstructured information pose. This particular process now enables businesses to utilize present knowledge that was otherwise indecipherable to computer processes.