Detecting Fine-Grained Emotions in Literature
Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature anal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7502 |
| _version_ | 1797592162478587904 |
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| author | Luis Rei Dunja Mladenić |
| author_facet | Luis Rei Dunja Mladenić |
| author_sort | Luis Rei |
| collection | DOAJ |
| description | Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis. |
| first_indexed | 2024-03-11T01:48:32Z |
| format | Article |
| id | doaj.art-e19882c263cf48abb3e8c7c6f17f424a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:48:32Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e19882c263cf48abb3e8c7c6f17f424a2023-11-18T16:07:17ZengMDPI AGApplied Sciences2076-34172023-06-011313750210.3390/app13137502Detecting Fine-Grained Emotions in LiteratureLuis Rei0Dunja Mladenić1Jožef Stefan Institute, 1000 Ljubljana, SloveniaJožef Stefan Institute, 1000 Ljubljana, SloveniaEmotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.https://www.mdpi.com/2076-3417/13/13/7502emotion detectionsemi-supervised learningweak-labelingpseudo-labelingbenchmark literature dataset |
| spellingShingle | Luis Rei Dunja Mladenić Detecting Fine-Grained Emotions in Literature Applied Sciences emotion detection semi-supervised learning weak-labeling pseudo-labeling benchmark literature dataset |
| title | Detecting Fine-Grained Emotions in Literature |
| title_full | Detecting Fine-Grained Emotions in Literature |
| title_fullStr | Detecting Fine-Grained Emotions in Literature |
| title_full_unstemmed | Detecting Fine-Grained Emotions in Literature |
| title_short | Detecting Fine-Grained Emotions in Literature |
| title_sort | detecting fine grained emotions in literature |
| topic | emotion detection semi-supervised learning weak-labeling pseudo-labeling benchmark literature dataset |
| url | https://www.mdpi.com/2076-3417/13/13/7502 |
| work_keys_str_mv | AT luisrei detectingfinegrainedemotionsinliterature AT dunjamladenic detectingfinegrainedemotionsinliterature |