GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian Model for Irony Type Identification

Abstract

Sarcasm refers to the use of words that have different literal and intended meanings. It represents the usage of words that are opposite of what is literally said, especially in order to insult, mock, criticise or irritate someone. These types of statements may be funny or amusing to others but may hurt or annoy the person towards whom it is intended. Identification of sarcastic phrases from social media posts finds its application in different domains like sentiment analysis, opinion mining, author profiling and harassment detection. We have proposed a model for the shared task iSarcasmEval - Intended Sarcasm Detection in English and Arabic by SemEval-2022 considering the language English. The Subtask A and Subtask C were implemented using a Convolutional Neural Network based classifier which makes use of ELMo embeddings. The Subtask B was implemented using Gaussian Naive Bayes classifier by extracting TF-IDF vectors. The proposed models resulted in macro-F1 scores of 0.2012, 0.0387 and 0.2794 for sarcastic texts in Subtasks A, B and C respectively.