Text analytics finds dynamic growth in e-discovery and customer feedback

"Previously we were using key words to identify relevant documents, but text analytics gives us a much clearer picture of the data set..."

Text analytics is a process for extracting information from documents. It is particularly useful in tasks requiring the analysis of large quantities of information that would be impossible to do manually. Linguistic and statistical techniques are used to classify and categorize the documents, and to discover concepts and relationships within them. Linguistic techniques include identifying synonyms, determining parts of speech and disambiguation, in which context is used to determine which of several possible alternative meanings a word might have. Statistical techniques include calculations of word frequency and proximity as well as pattern analysis.

E-discovery application
Although text analytics has long been used for drawing meaning out of large quantities of data in many fields, without a doubt the most dynamic areas right now are e-discovery and analysis of customer information from social media.

LeClairRyan is a law firm that offers corporate and litigation services, including e-discovery collection, review and production services. The firm uses a variety of e-discovery platforms but most often turns to Relativity from kCura, delivered on demand through kCura hosting partner Planet Data Discovery Management Solutions. Relativity is an e-discovery software solution for review and management of both electronic and paper-based documents. LeClairRyan recently added Relativity's text analytics to its platform.

"One of the capabilities we use regularly is document clustering," says William Belt, team leader for e-discovery practice at LeClairRyan. "In the past, we reviewed documents in a linear fashion-for example, in chronological sequence. Having the documents clustered by topic is more efficient because the reviewers do not have to shift gears as much."

Clustering can also help identify relevancy, so that groups of documents that are likely to be highly relevant are together. "This ability helps with early case assessment," Belt says. "Previously we were using key words to identify relevant documents, but text analytics gives us a much clearer picture of the data set." Another benefit of clustering is quickly identifying potential areas of risk. "We can prioritize documents according to their likely level of risk," he adds, "which puts us in a better strategic position in the review process."

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Source: kmworld.com
By: Judith Lamont Ph. D.