An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis.
Entropy (Basel)
; 25(5)2023 May 13.
Article
in En
| MEDLINE
| ID: mdl-37238549
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Entropy (Basel)
Year:
2023
Type:
Article
Affiliation country:
China