Sentiment: Plenty of buzz, but focused in the wrong direction

  2 m, 30 s

Ever since the New York Time's article about sentiment scoring, published a couple of weeks ago, there has been a pretty constant stream of people jumping in and demonizing automated sentiment or trying to pedal its eventual takeover of the free world. It felt a lot like political media coverage to me, lots of opinions but very few of them taking an honest look at the real problems and real solutions. Kudos to Nathan Gilliatt for putting out the list of many of these posts (see the list here). It's obvious that we all have axes to grind and software or services to sell, but focusing on the accuracy of automated sentiment is the wrong place to go. To remove any doubts let me state for the record that a machine based system will never score any random piece of social content as well as a human will. People simply have too much context in their brains, and there is no way a machine is going to match that. Given the preceding, one my ask whether I believe that automated sentiment is doomed to failure? The answer is NO, we need to use automated sentiment in ways that you can't provide with humans. I'll illustrate my point by focusing on one of the recent posts about sentiment, Sentiment analysis for online content: Honest? from CyTRAP Labs. The post wasn't particularly favorable to automated sentiment, but was one of the first posts I've read that asked the right question... Is this story relevant? If you're monitoring social media sources, then what you really want to know is: What's happening that I need to worry about? Text Analytics and automated sentiment is very good at answering these "trend spotting" questions. In fact, machines are an essential piece in providing trend spotting. Sentiment Analysis has made rapid inroads in the financial services industry because users don't care about the tone of each story, they care about the effect of a bunch of stories on the market as a whole. A number of our financial services customers are making lots of money trading equities based in part on sentiment trends, and that should say something about its validity. If you're job is to monitor a brand in social media then the trends and patterns are what you should be worried about, and automated analysis is great for this. If an automated system is only 70% accurate, it's still going to get the overall trend (up or down) correct for a given brand, and then the humans should always step in and provide the detailed analysis of that trend, including the identification and correction of the posts where the machine got it wrong. Let automated sentiment point the way, but trust humans to provide the detailed analysis that requires a few neurons. Automated systems will never beat humans on a story by story basis, so let's stop worrying about that and use them to provide services that humans can't afford to do.

Categories: Sentiment Analysis, Social Media, Technology, Text Analytics