shiv213/Automatic-Sarcasm-Detection-Twitter

Automatic Sarcasm Detection

*The Shared Task (2nd FigLang Workshop at ACL 2020) is now over. Thanks a lot, participants :)*

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...". Your 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* :

For Test, we will be providing you the *response and the context. We will also provide the id* (i.e., identifier) to report the the results.

*Submission Instructions* : Please follow the given link

*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.

*Note*: Since we have collected our training data from popular social media platforms a large portion of the utterances are on controversial and/or political and social topics. Although we have pre-processed the training data and lightly edited to remove contentious text, many utterances still contain controversial perspectives (of the users) and informal language.