On the Value of Customized Sentiment Analysis

August 26, 2014

Natural language processing—one of its most-discussed functions in business is sentiment analysis. Over at the SmartData Collective, Lexalytics’ Scott Van Boeyen tells us “Why Sentiment Analysis Engines Need Customization.” The short answer: slang. The write-up explains:

The problem with sentiment analysis is sometimes it’s wrong.[…]

“Oh man, that was nasty!” Is this sentence positive or negative? Surely, it must be negative. “Nasty” is a negative word, and everything else in this sentence is neutral. Final answer, negative! Drum roll…. Wrong! It’s positive.

The person who said this used the American slang definition of nasty, which has positive sentiment. There is absolutely no way to know by reading the sentence. So, if you (a human) were just tricked by reading this article, how is a machine supposed to figure it out? Answer: Tell the engine what’s positive and what’s negative.

High quality NLP engines will let you customize your sentiment analysis settings. “Nasty” is negative by default. If you’re processing slang where “nasty” is considered a positive term, you would access your engine’s sentiment customization function, and assign a positive score to the word.

The man has a point. Still, we are left with a few questions: How much more should one expect to pay for a customization feature? Also, how long does it take to teach an NLP platform comprehensive alternate vocabulary? How does one decide what slang to include—has anyone developed a list of suggestions? Perhaps one could start by consulting the Urban Dictionary.

Cynthia Murrell, August 26, 2014

Sponsored by ArnoldIT.com, developer of Augmentext


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