Multi-modal Chinese Text Emotion Metaphor Computation Based on Mutual Information and Information Entropy
Authors: 
Zhifa Zeng, Yuhang LiAuthors Info & Claims
Zhifa Zeng, Yuhang LiAuthors Info & ClaimsArticle No.: 111, Pages 1 - 18
Abstract
Metaphor is to express another thing through one thing, it is not only a rhetorical means, but also embodies a kind of analogical cognition and way of thinking of people. In recent years, metaphors have made more and more progress in Chinese language recognition, so their status has gradually risen. Chinese language sentiment analysis is a relatively difficult problem in language processing. Chinese language semantics is ambiguous and complex, so it is a great challenge to construct Chinese Emotion structure. In this paper, Chinese nouns and Chinese verbs are used as test samples to conduct metaphor test calculation. The test results of mutual information and information entropy show that for the sample metaphor calculation, the correct rate of individual words is less than 70%. The ratio between the macro-average of gerund words and the baseline fluctuates relatively large. The metaphor recognition of nouns is about 20% higher than the original test results and about 10% higher for verbs. It shows that under the action of mutual information and information entropy, the metaphor recognition performance of the basic structure of Chinese Emotion constructed from the perspective of metaphor is improved, and further illustrates the feasibility of mutual information and information entropy algorithm for building the basic structure.
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Index Terms
- Multi-modal Chinese Text Emotion Metaphor Computation Based on Mutual Information and Information Entropy
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Published In
August 2024
343 pages
EISSN:2375-4702
DOI:10.1145/3613611
- Editor:
- Imed Zitouni,
- Guest Editors:
- Deepak Kumar Jain,
- Thierry Boumans,
- Stefano Berretti
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 August 2024
Online AM: 23 June 2023
Accepted: 12 June 2023
Revised: 29 May 2023
Received: 30 March 2023
Published in TALLIP Volume 23, Issue 8
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