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Can a machine detect sarcasm? Yeah, right

James Kobielus | July 30, 2014
Applying analytics to social media? Good luck -- not all words can be taken at face value. Natural language processing helps, but it's no panacea.

If you're trying to gauge broad shifts in customer sentiment, as evidenced by increasingly barbed statements, sarcasm analytics technologies might be exactly what you need. A recent article discusses how this application of NLP (natural language processing) and machine learning can support brand management initiatives. According to author Erin Carson, sarcasm analytics "can provide a chance to learn about things like product issues, service issues, unexpected uses of a product, areas where the brand is spending too much time -- or not enough -- or general feelings from customers and potential customers."

Perfection is too much to ask from automated sarcasm analytics. The article cites an industry analyst who says keyword analysis is typically 60 to 65 percent accurate, while NLP raises that to 80 to 85 percent. But the toughest 15 percent of cases require human judgment to distinguish sincerity from snark. Plus, some humans' judgment can't be trusted; they may be utterly clueless in reading the intentions of others.

If you ask me, perfection is irrelevant when you're trying to gauge somebody's deeper intentions from their overt verbalizations. If 85 percent of the time you've determined that what they're putting forth is sincere, you can safely assume the vaguer, grayer areas in words conceal no daggers.

After all, most people are not clever (or diabolical) enough to double-edge their words 15 percent of the time, while otherwise remaining totally straightforward and transparent.

Sarcasm is not the same as treachery. More often than not, what it expresses is a mere blip of momentary irritation in an otherwise satisfying relationship.

At least that's the way it works in my marriage.

Source: InfoWorld

 

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