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Software Recognizes Sarcastic Tweets
An anonymous reader writes "Even humans sometimes fail to recognize sarcasm and irony; can machines do better? An algorithm that identifies sarcastic tweets (PDF) on Twitter and sarcastic sentences in product reviews on Amazon will be presented next week in the International Conference for Weblogs and Social Media in Washington, DC, and in the Computational Natural Language Learning in Sweden in July. A team from the Hebrew University, Israel, has developed an algorithm that identifies sarcastic sentences by using a machine learning technique in which a small number of sarcastic sentences act as seeds for the software to learn and generalize upon. The algorithm can then identify sarcastic sentences that are nothing like the examples. The variety of recognized sarcastic sentences is impressive, though the results are not perfect. But again, we don't do it so well ourselves, do we?"
Weight of various patterns and features. We present here a deeper look on some examples. A classic example of a sarcastic comment is: "Silly me, the Kindle and the Sony eBook can’t read these protected formats. Great!". Some of the patterns it contains are ...
You know DRM is pervasive as a very serious consumer problem when statistical research papers recognize user dissatisfaction with it as a classic example of sarcasm that floods reviews.
My work here is dung.
This may help people with autism and Asperger's Syndrome recognize satire.
The problem with that is that in American sitcoms, verbal irony is accompanied by non-verbal cues like facial expression, tone of voice, or, ugh, laugh tracks. Take away the cues, and deliver the sarcasm in a deadpan manner, and tons of people in the USA are completely unable to catch it, neurotypical or not.
Are you adequate?