Leveraging the power of transformers for guilt detection in text
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917567710363648 |
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| author | Meque, Abdul Gafar Manuel Angel, Jason Sidorov, Grigori Gelbukh, Alexander |
| author_facet | Meque, Abdul Gafar Manuel Angel, Jason Sidorov, Grigori Gelbukh, Alexander |
| contents | In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like "shame" through qualitative analysis of results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07414 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging the power of transformers for guilt detection in text Meque, Abdul Gafar Manuel Angel, Jason Sidorov, Grigori Gelbukh, Alexander Computation and Language In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like "shame" through qualitative analysis of results. |
| title | Leveraging the power of transformers for guilt detection in text |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.07414 |