Automatic Sarcasm Detection

Please refer to reddit and twitter sub-directories for further references on datasets.

For Twitter and Reddit, training and testing datasets are provided for sarcasm detection tasks in jsonlines format.

Each line contains a JSON object with the following fields :

• label : SARCASM or NOT_SARCASM
• id: String identifier for sample. This id will be required when making submissions.
  • ONLY in test data
• response : the sarcastic response, whether a sarcastic Tweet or a Reddit post
• context : the conversation context of the response
  • Note, the context is an ordered list of dialogue, i.e., if the context contains three elements, c1, c2, c3, in that order, then c2 is a reply to c1 and c3 is a reply to c2. Further, if the sarcastic response is r, then r is a reply to c3.

For instance, for the following training example :

"label": "SARCASM", "response": "Did Kelly just call someone else messy? Baaaahaaahahahaha", "context": ["X is looking a First Lady should . #classact, "didn't think it was tailored enough it looked messy"]

The response tweet, "Did Kelly..." is a reply to its immediate context "didn't think it was tailored..." which is a reply to "X is looking...". Our goal is to predict the label of the "response" while also using the context (i.e, the immediate or the full context).

Dataset size statistics :

	Train	Test
Reddit	4400	1800
Twitter	5000	1800

For Test, the response and the context are provided along with id (i.e., identifier) to report the the results.

Main References:

A Report on the 2020 Sarcasm Detection Shared Task. Debanjan Ghosh, Avijit Vajpyee, Smaranda Muresan. Proceedings of the Second Workshop on Figurative Language Processing.