CancerEmo: A Dataset for Fine-Grained Emotion Detection
Tiberiu Sosea, Cornelia Caragea
Abstract
Emotions are an important element of human nature, often affecting the overall wellbeing of a person. Therefore, it is no surprise that the health domain is a valuable area of interest for emotion detection, as it can provide medical staff or caregivers with essential information about patients. However, progress on this task has been hampered by the absence of large labeled datasets. To this end, we introduce CANCEREMO , an emotion dataset created from an online health community and annotated with eight fine-grained emotions. We perform a comprehensive analysis of these emotions and develop deep learning models on the newly created dataset. Our best BERT model achieves an average F1 of 71%, which we improve further using domain-specific pretraining.
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Install the CLIlune papers fulltext c55f995c-b82f-45ac-b7a7-05ae766329caCited by top-tier papers6
- Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media PostsHongli Zhan, Tiberiu Sosea, Cornelia Caragea, Junyi Jessy LiEMNLP 2022 · 8 citations
- Dimensional Emotion Detection from Categorical EmotionSungjoon Park, Jiseon Kim, Seonghyeon Ye, Jaeyeol Jeon et al.EMNLP 2021 · 3 citations
- Unsupervised Extractive Summarization of Emotion TriggersTiberiu Sosea, Hongli Zhan, Junyi Jessy Li, Cornelia CarageaACL 2023 · 3 citations
- Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and ClassificationPinyi Zhang, Jingyang Chen, Junchen Shen, Zijie Zhai et al.EMNLP 2024 · 1 citation
- SRL4E - Semantic Role Labeling for Emotions: A Unified Evaluation FrameworkCesare Campagnano, Simone Conia, Roberto NavigliACL 2022
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