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An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis.

Shufeng Xiong¹, Xiaobo Fan¹, Vishwash Batra²

¹College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China.

Entropy (Basel, Switzerland)

|May 27, 2023

Summary

This study introduces a new Chinese Textual Affective Structure (CTAS) dataset from Weibo, enhancing AI

Keywords:

Chinese benchmark datasets affective computing affective structure corpus annotation

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Area of Science:

Artificial Intelligence
Natural Language Processing
Computational Linguistics

Background:

Affective understanding of language is crucial for AI.
Large-scale annotated datasets are needed for Chinese Textual Affective Structure (CTAS) analysis.
Existing CTAS datasets are scarce, limiting research progress.

Purpose of the Study:

Introduce a novel benchmark dataset for Chinese Textual Affective Structure (CTAS).
Facilitate higher-level document analysis through comprehensive affective structure labeling.
Advance research in AI-driven affective language understanding.

Main Methods:

Developed a Weibo-based dataset, leveraging a popular Chinese social media platform.
Incorporated the most comprehensive affective structure labels currently available.
Proposed a maximum entropy Markov model integrated with neural network features.

Main Results:

The new CTAS dataset is based on Weibo, China's leading social media platform.
The dataset features the most extensive affective structure labels to date.
The proposed maximum entropy Markov model demonstrated superior performance over baseline models.

Conclusions:

The introduced Weibo-based CTAS dataset addresses the scarcity of resources in affective AI.
The comprehensive labeling and advanced model offer a significant contribution to the field.
This work promotes further research in affective understanding of Chinese social media text.