Xiong, S, Fan, X, Batra, V, Zeng, Y, Zhang, G, Xi, L, Liu, H and Shi, L (2023) An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis. Entropy, 25 (5). 794 - 794. ISSN 1099-4300

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<jats:p>Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. However, there are very few published datasets for CTAS. This paper introduces a new benchmark dataset for the task of CTAS to promote development in this research direction. Specifically, our benchmark is a CTAS dataset with the following advantages: (a) it is Weibo-based, which is the most popular Chinese social media platform used by the public to express their opinions; (b) it includes the most comprehensive affective structure labels at present; and (c) we propose a maximum entropy Markov model that incorporates neural network features and experimentally demonstrate that it outperforms the two baseline models.</jats:p>

Item Type: Article
Additional Information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Subjects: Q Science > Q Science (General)
Q Science > Q Science (General) > Q335 Artificial Intelligence
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 18 May 2023 15:40
Last Modified: 18 May 2023 15:40
URI: https://eprints.keele.ac.uk/id/eprint/12660

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