3 ms·

Yeah, so much fun.

• agibsonccc 13y ago
  Clever. hehe That aside, has anyone saw any recent works on detecting sarcasm? There's not much advancement in this that I've seen.
  • brianshaler 13y ago
    This deep learning method is an example from going from bag-of-words to document level analysis. To detect sarcasm, you'd have to make another leap from document analysis to personality modeling. Humans can detect sarcasm because they^H^H^H^Hwe can build a mental model of what is expected to be said—by a particular author or about a specific topic. If a statement is sufficiently contradictory to our mental model and we are sufficiently confident of the predicted sentiment, we know it's sarcasm. Without knowing who said something or in what context it was said, even humans would fail to accurately detect sarcasm. Here are some hasty examples that could vary, depending on the author or situation: "Michael Bay really outdid himself on that one." "That Kanye West song is so well-written." "It'd fun to build that PHP."
    • agibsonccc 13y ago
      Very good point. I love the way you framed that. I took some very cursory attempts at doing it at the sentence level based on ngram representations, obviously this was limited. You also bring up a very good point about humans being able to barely detect it. It's hard to infer from text without tone a good portion of the time. It'd be neat to see what we could do with tone as an input feature (obviously we couldn't get that most of the time) but maybe if enough tech becomes available that voice inputs are common, sarcasm can be a relevant enough problem to solve. In general, I know it's not really worth it to try to solve (that I could think of immediately anyways) aside from "just because". That being said: ambiguity is ambiguous. What can you do aside from approximate as best you can?