| Challenge: | Existing datasets for language-based emotion classification are limited and small . existing datasets lack quality annotations for many different emotion categories . |
| Approach: | They propose to use a large manually annotated dataset to study emotion expressions . they conduct transfer learning experiments with existing emotion benchmarks to test their model . |
| Outcome: | The proposed model achieves an average F1-score of .46, leaving room for improvement. |
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