Publication Type : Conference Paper
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Source : Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Url : https://aclanthology.org/2022.semeval-1.115/
Campus : Amaravati, Coimbatore
School : School of Artificial Intelligence, School of Artificial Intelligence - Coimbatore
Department : Center for Computational Engineering and Networking (CEN)
Year : 2022
Abstract : This paper describes the submission of the team Amrita_CEN to the shared task on iSarcasm Eval: Intended Sarcasm Detection in English and Arabic at SemEval 2022. We employed machine learning algorithms towards sarcasm detection. Here, we used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, and Decision Tree along with the Random Forest ensemble method. Additionally, feature engineering techniques were applied to deal with the problems of class imbalance during training. Among the models considered, our study shows that the SVM, logistic regression and ensemble model Random Forest exhibited the best performance, which was submitted to the shared task.
Cite this Research Publication : Ajayan, A.K., Mohanan, K., Anugraha, S., Premjith, B., Soman, K. P., "Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling, (2022) SemEval 2022," 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop, pp. 834-839.