2022/04/30 by Shaily Desai, Atharva Kshirsagar, Desai, Shaily +7
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2205.00283
openalex publication_date 2022/04/30 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations. We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.