The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus
Metaphorical expressions are frequently used to convey emotions in human communication. However, there is limited research on the detection of emotionality in metaphorical expressions, although a number of studies have focused on sentiment analysis and metaphor detection separately. We, therefore, a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8534384/ |
| _version_ | 1818428926553227264 |
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| author | Dongyu Zhang Hongfei Lin Puqi Zheng Liang Yang Shaowu Zhang |
| author_facet | Dongyu Zhang Hongfei Lin Puqi Zheng Liang Yang Shaowu Zhang |
| author_sort | Dongyu Zhang |
| collection | DOAJ |
| description | Metaphorical expressions are frequently used to convey emotions in human communication. However, there is limited research on the detection of emotionality in metaphorical expressions, although a number of studies have focused on sentiment analysis and metaphor detection separately. We, therefore, attempt to identify emotions in Chinese metaphorical texts. We first construct a manual corpus with an annotation scheme, which contains annotations of metaphor, and emotional categories. We then use the corpus as a train-and-test set to identify the emotions in metaphorical expressions automatically with three methods. The first method is based on a field dictionary and field conflict. The second method is based on a support vector machine. The third method is based on deep learning, and it applies the long short-term memory model to identify the emotion of metaphor. The experimental results show that the third method performs better in identifying metaphor tasks, while the first method works better for emotion classification. In this paper, we compared the strength of heuristic, stochastic, and deep learning approaches, which contributes to a challenging natural language processing issue: the detection of emotionality in metaphor. |
| first_indexed | 2024-12-14T15:09:23Z |
| format | Article |
| id | doaj.art-f1a0e8b050dd4285b0dca52e30a7de62 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:09:23Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f1a0e8b050dd4285b0dca52e30a7de622022-12-21T22:56:36ZengIEEEIEEE Access2169-35362018-01-016712417124810.1109/ACCESS.2018.28812708534384The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese CorpusDongyu Zhang0https://orcid.org/0000-0002-7683-5560Hongfei Lin1Puqi Zheng2Liang Yang3Shaowu Zhang4School of Software, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaMetaphorical expressions are frequently used to convey emotions in human communication. However, there is limited research on the detection of emotionality in metaphorical expressions, although a number of studies have focused on sentiment analysis and metaphor detection separately. We, therefore, attempt to identify emotions in Chinese metaphorical texts. We first construct a manual corpus with an annotation scheme, which contains annotations of metaphor, and emotional categories. We then use the corpus as a train-and-test set to identify the emotions in metaphorical expressions automatically with three methods. The first method is based on a field dictionary and field conflict. The second method is based on a support vector machine. The third method is based on deep learning, and it applies the long short-term memory model to identify the emotion of metaphor. The experimental results show that the third method performs better in identifying metaphor tasks, while the first method works better for emotion classification. In this paper, we compared the strength of heuristic, stochastic, and deep learning approaches, which contributes to a challenging natural language processing issue: the detection of emotionality in metaphor.https://ieeexplore.ieee.org/document/8534384/Emotionality of metaphorunderstanding of semanticsdeep learningfield conflict |
| spellingShingle | Dongyu Zhang Hongfei Lin Puqi Zheng Liang Yang Shaowu Zhang The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus IEEE Access Emotionality of metaphor understanding of semantics deep learning field conflict |
| title | The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus |
| title_full | The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus |
| title_fullStr | The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus |
| title_full_unstemmed | The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus |
| title_short | The Identification of the Emotionality of Metaphorical Expressions Based on a Manually Annotated Chinese Corpus |
| title_sort | identification of the emotionality of metaphorical expressions based on a manually annotated chinese corpus |
| topic | Emotionality of metaphor understanding of semantics deep learning field conflict |
| url | https://ieeexplore.ieee.org/document/8534384/ |
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