Papers with emotion classification task
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach (2021.naacl-srw)
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| Challenge: | Existing methods to classify Bengali text into six basic emotions are infancy for resource-constrained languages like English, Arabic, Chinese and French. |
| Approach: | They propose a transformer-based technique to classify Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. |
| Outcome: | The proposed technique outperforms all other techniques by achieving highest weighted f_1-score on the test data. |
SwahBERT: Language Model of Swahili (2022.naacl-main)
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| Challenge: | Social media and Internet forums are valuable sources of citizens’ opinions, which can be analyzed for community development and user behavior analysis. |
| Approach: | They present a pre-training and annotated datasets of Swahili and an emotion classification datasets that are manually annotating by two native Swahils. |
| Outcome: | The proposed model outperforms existing monolingual language model in almost all downstream tasks. |
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision (2020.lrec-1)
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| Challenge: | Existing methods to annotate unlabeled data with emotions are expensive and time-consuming. |
| Approach: | They propose an annotation procedure that leverages Korean emotion lexicons and Korean-specific emotion features to annotate unlabeled data. |
| Outcome: | The proposed procedure compares with the KTEA dataset and a large-scale emotion-labeled dataset. |