AI reads your tweets and spots when you’re being sarcastic
AI reads your tweets and spots when you’re being
sarcastic
That is so funny, not
By Edd Gent DAILY NEWS 4 August 2016
Without a helpful hashtag, picking up on sarcasm online
can be hard even for humans. For literal-minded computers, it’s often a major
headache. But now a machine learning system can automatically recognise when
individuals are being sarcastic.
Mining people’s comments on social media is big business.
Advertisers track people’s attitudes and moods, companies and governments
follow public opinion. But people being sarcastic and saying the opposite of
what they actually is super tricky to pick up on. So concerned is the US Secret
Service that it listed sarcasm detection as a desired feature in a 2014 tender
for a social media analytics service.
Clued up or clueless?
Computers can exploit small textual clues, such as use of
exclamation marks, to detect sarcasm with some degree of accuracy. But without
context, it is hard identify the tone of a comment. “Isn’t Obama great!!”
clearly means different things coming from a Republican or a Democrat.
Looking at information like the relationship between a
comment’s author and audience or where the comment is posted online makes a big
difference, pushing the accuracy up to around 80 per cent. But coding these
features by hand is laborious, and selecting which to use depends largely on
intuition.
Now Silvio Amir at the University of Lisbon, Portugal,
and colleagues have turned to machine learning. They have trained their system
to identify sarcasm on Twitter simply by looking at a user’s past tweets. “We
can get away without looking at all this external information,” says Amir.
Who you are
Using just these tweets, the system builds up a picture
of a person that is rich enough to guess when they are being sarcastic. “It
intuitively makes sense,” says Amir. “Tell me what you talk about and I can
tell you who you are.”
Amir’s system predicts sarcasm with an accuracy of 87 per
cent, slightly better than existing approaches. However, by learning to detect
sarcasm without human input, the system should be very easy to use.
Amir also says the approach should work for any language
and any online platform where posting history is available. “The key innovation
is realising you can build a model of the user merely based on what they have
said in the past,” says Amir. The team will present their findings next week at
CoNLL, a Google-sponsored conference on natural language processing in Berlin.
Mark Carman at Monash University in Melbourne, who
studies sarcasm detection, thinks it would be relatively straightforward to
integrate the approach with other types of social media analysis, such as
tracking people’s emotions or stock market trends.
What’s more, dealing with sarcasm would be a great help
for marketers and customer service teams, says Carman – not to mention virtual
assistants like Apple’s Siri.
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