How text analytics unlocks big data value

Big data is currently basking under the glow of the spotlights these days.

However, when it comes to unearthing insights and value out of information it is analytics that plays the lead role.

Since a huge amount of data is textual in nature, the job of obtaining value from it falls on textual analytics, according to technology writer David Hill of in his recent blog. Hill recently attended the Text and social Media Analytics Summit in Cambridge, Mass.

Text analytics refers to refers to deriving high quality information from text usually using methods that involves structuring input text, parsing, getting patterns from structured data and evaluating and interpreting the output. Text analytics typically involves tasks such as text categorization, text clustering and concept extraction.

Traditionally analytics has focused on structured data such as conducting analysis on data warehouse. However, much of big data falls under the category of unstructured data.

Unstructured data, Hill said, tends to respond to analytical techniques such as text analytics rather than the type of analytics applied to SQL data

In the conference, Ralph Winters, of health care insurance company Emblem Health, showed how mapping unstructured data with structured data using a full text search with a weighted word matrix and other analyses can be useful in conducting sentiment analysis.


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Another presenter, Sergei Ananyan, CEO of data mining firm, Megaputer Intelligence Inc., said that text analytics is leveraging machine learning, semantic analysis and deep linguistic parsing for use in loan default analysis and sentiment analysis.

Text analytics can also be used in e-discovery, which involved the examination of electronic data for evidence in legal cases.

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Jim Love, Chief Content Officer, IT World Canada

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