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12/16/2010
01:54 PM
Seth Grimes
Seth Grimes
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Five Ways To Fool A Twitter Sentiment Tool

Do sentiment-analysis tools pass the accuracy test? Here are five tests along with results using freely available products.

Like many social-media analyses, the idea for this one originated on a social platform. In this case it was Twitter, the most accessible, easy, and free-wheeling of them all and hence a great place to exchange information and opinions.

Tweets (and other social and online postings) have immense business value, in particular as a "voice of the customer" information source about products and services and also about politics, family, and just about every other aspect of our daily lives. Their subjectivity -- they voice opinions and not just facts -- is what makes them particularly valuable.

How do we get at this business value, at sentiment especially, and how do we make sure we're doing it reasonably well? You do need software to get a complete picture of the online universe. There are dozens of tools on the market, some very good, some not so strong. Accuracy, as measured by precision, recall, and relevance, is essential. I'll suggest a few simple tests that can help you assess the precision (proportion of cases correctly classified) of tools you may be considering, focusing on Twitter sentiment analysis.

One of my tweeple, @Pythonner (a.k.a. David Nadeau) posted a lookout, Twitter + sentiment analysis, nicely integrated: http://www.tweetfeel.com/. The Tweetfeel tool from Conversition does make it easy to view Twitter-message sentiment via what the company characterizes as "real-time Twitter search with feelings using insanely complex sentiment analysis."

Accurate automated sentiment analysis is insanely difficult given the complexity of human language and expression. As expert systems pioneer Edward A. Feigenbaum observed, "Reading from text in general is a hard problem, because it involves all of common sense knowledge."

Text is full of subjectivity. There is no text-analysis problem harder than correctly parsing attitudes, opinions, feelings and emotions.

Five Tests

My first test is sentiment classification for someone who has recently died. Tools trip easily on that one. Tweetfeel is no exception, as seen in a search on Elizabeth Edwards.

The tweet, "ummm I feel totally @#$%!... elizabeth edwards lost her battle with cancer??? omg!!!!! how sad" is NOT, as rated by Tweetfeel, negative.

The folks from Conversition did make clear, "TweetFeel is intended as a quick, fun tool. It's a 'give back to the community' application."

Prompted by another of my tweeple, @yehaskel (a.k.a. David Yehaskel), I'll offer four more tests, essentially quick and easy ways to trip up (most) Twitter sentiment tools but also good starters for any evaluation of the more capable tools on the market, albeit not available for open trial.

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