ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models

Abstract

Robust sarcasm detection is critical for creating artificial systems that can effectively perform sentiment analysis in written text. In this work, we investigate AI approaches to identifying whether a text is sarcastic or not as part of SemEval-2022 Task 6. We focus on creating systems for Task A, where we experiment with lightweight statistical classification approaches trained on both GloVe features and manually-selected features. Additionally, we investigate fine-tuning the transformer model BERT. Our final system for Task A is an Extreme Gradient Boosting Classifier (XGB Classifier) trained on manually-engineered features. Our final system achieved an F1-score of 0.2403 on Subtask A and was ranked 32 of 43.