ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models
Samantha Huang, Ethan Chi, Nathan Chi · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
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 manuallyselected 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.