ARCHIVED CONTENT
You are viewing ARCHIVED CONTENT released online between 1 April 2010 and 24 August 2018 or content that has been selectively archived and is no longer active. Content in this archive is NOT UPDATED, and links may not function.By Judith Lamont, Ph.D.
From keyword-based analysis to clustering of concepts and sentiment analysis, text analytics has grown increasingly sophisticated. The next advance will be more influential, yet its mechanism will be much less visible. “The wave of the future is to make things easier for users,” says Matt LeGare, product manager for Attensity. “The early adopters have been willing to push through the technology and big data to process a large corpus of information, but for others, usability has to improve.”
The mechanism will be less visible because more of the computer’s work will be going on behind the scenes. “Right now, users typically have to provide a lot of manual input to fine-tune text analytics—for example, with categorization,” says LeGare, “but we are aiming for an automated categorization rate of 80 to 100 percent.” That goal can be achieved only if the text analytics solution becomes more intelligent, has the ability to make inferences and provides or seeks out knowledge that is relevant to users.
Read the complete article at Text analytics: greater usability, less time to insight