This thesis demonstrates that hierarchical classifiers that organize a classifica-tion task across multiple, sequential stages substantially improve emotion clas-sification accuracy compared to flat classifiers that classify text in a single step, effectively recognizing underrepresented emotions and classifying similar emo-tions within Japanese social media texts, using the WRIME dataset. The study also investigates different training data formats—emotion intensity lists and sin-gle primary emotion labels—and reveals that while classifiers trained with lists generally achieve better overall performance, single-label classifiers excel inidentifying underrepresented emotions and those expressed with high intensity.We also explore the impact of sentence length and emotion intensity on classifierperformance, finding that hierarchical classifiers are more effective with textsthat exhibit higher emotional intensities and shorter sentences. These findingsdemonstrate the potential of hierarchical approaches to address the complexities of emotion classification tasks, contributing valuable insights into the develop-ment of more effective natural language processing systems. Future research directions include enhancing the computational efficiency of hierarchical classi-fiers and comparing the hierarchical classifiers implemented with various deep learning approaches.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-531396 |
| Date | January 2024 |
| Creators | Kurita, Masaki |
| Publisher | Uppsala universitet, Institutionen för lingvistik och filologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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