Answers.Body: <p>There has been a recent work in the same domain where neural networks(CNNs to be accurate) are used for the same purpose. Some info. about the research is:</p>
  
  <blockquote>
    <p>To learn that context, the paper describes a method by which the
    neural network finds the user’s “embeddings” — i.e. contextual cues
    like the content of previous tweets, related interests and accounts,
    and so on. It uses these various factors to plot the user with others,
    and (ideally) finds that they form relatively well-defined groups.</p>
  </blockquote>
  
  <p>So, the paper uses CNNs, word and user embeddings for detecting sarcasm in text. There is also a <a href="https://techcrunch.com/2016/08/04/this-neural-network-tries-to-tell-if-youre-being-sarcastic-online/" rel="nofollow">Techcrunch article</a> on that.</p>
  
  <p>The paper uses sentiment of the tweet and compares with that of the other similar tweets:</p>
  
  <blockquote>
    <p>If the sentiment of the tweet seems to disagree with the bulk of what
    is expressed by similar users, there’s a good chance sarcasm is being
    employed.</p>
  </blockquote>
  
  <p><a href="http://arxiv.org/pdf/1607.00976v2.pdf" rel="nofollow">Link to the paper</a></p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-05T04:52:42.947Z
Answers.LastActivityDate: 2016-08-05T04:52:42.947Z
Answers.UserDisplayName: Dawny33
Answers.Body: <p>The following survey article by researchers from IIT Bombay summarizes recent advances in sarcasm detection: <a href="https://arxiv.org/abs/1602.03426" rel="noreferrer">Arxiv link</a>.</p>
  
  <p>In reference to your question, I do not think it is considered either extraordinarily difficult or open-ended. While it does introduce ambiguity that computers cannot yet handle, Humans are easily able to understand sarcasm, and are thus able to label datasets for sarcasm detection.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-04T21:45:48.653Z
Answers.LastActivityDate: 2016-08-04T21:45:48.653Z
Answers.UserDisplayName: Harsh
Body: <p>Identifying sarcasm is considered one of the most difficult open-ended problems in the domain of ML and NLP/NLU.</p>
  <p>So, was there any considerable research done on that front? If yes, then what is the accuracy like? Please, also, explain the NLP model briefly.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-03T09:07:02.650Z
Comments.Text: http://www.popsci.com/technology/article/2010-05/computer-algorithm-can-recognize-sarcasm-which-soooo-cool
Comments.UserDisplayName: Mithical
Tags: <natural-language-processing><reference-request><natural-language-understanding><semantics>
Title: What research has been done in the domain of "identifying sarcasm in text"?
UserDisplayName: Dawny33
ThreadText: # What research has been done in the domain of "identifying sarcasm in text"?
  
  (Asked by: username_0 on 2016-08-03)
  
  Identifying sarcasm is considered one of the most difficult open-ended problems in the domain of ML and NLP/NLU.
  
  So, was there any considerable research done on that front? If yes, then what is the accuracy like? Please, also, explain the NLP model briefly.
  
  **Tags:** natural-language-processing, reference-request, natural-language-understanding, semantics
  > **Comments:**
  > **username_1**: http://www.popsci.com/technology/article/2010-05/computer-algorithm-can-recognize-sarcasm-which-soooo-cool  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2016-08-04. Score: 5 | Accepted answer
  
  The following survey article by researchers from IIT Bombay summarizes recent advances in sarcasm detection: [Arxiv link](https://arxiv.org/abs/1602.03426).
  
  In reference to your question, I do not think it is considered either extraordinarily difficult or open-ended. While it does introduce ambiguity that computers cannot yet handle, Humans are easily able to understand sarcasm, and are thus able to label datasets for sarcasm detection.
  
  
  ---
  
  ### Answer by username_0 on 2016-08-05. Score: 2
  
  There has been a recent work in the same domain where neural networks(CNNs to be accurate) are used for the same purpose. Some info. about the research is:
  
  > To learn that context, the paper describes a method by which the
  > neural network finds the user’s “embeddings” — i.e. contextual cues
  > like the content of previous tweets, related interests and accounts,
  > and so on. It uses these various factors to plot the user with others,
  > and (ideally) finds that they form relatively well-defined groups.
  
  So, the paper uses CNNs, word and user embeddings for detecting sarcasm in text. There is also a [Techcrunch article](https://techcrunch.com/2016/08/04/this-neural-network-tries-to-tell-if-youre-being-sarcastic-online/) on that.
  
  The paper uses sentiment of the tweet and compares with that of the other similar tweets:
  
  > If the sentiment of the tweet seems to disagree with the bulk of what
  > is expressed by similar users, there’s a good chance sarcasm is being
  > employed.
  
  [Link to the paper](http://arxiv.org/pdf/1607.00976v2.pdf)
  
  
  ---
  
  