一个结构化的情绪分析数据集,基于来自不同领域的公众评论
Zhongliang Wei1, Shunxiang Zhang1
1School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, China.
Data in brief
|March 5, 2024
概括
本文介绍了一个大规模的中国情绪分析数据集,包含22个领域超过20万条社交媒体评论. 数据集是精心注释用于培训和验证自然语言处理模型.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 情绪分析对于了解公众论至关重要.
- 现有的中国情绪分析数据集在范围和规模上有限.
- 社交媒体为情绪分析提供了丰富的用户生成内容来源.
研究的目的:
- 为中文语言引入一个全面的,结构化的情绪分析数据集.
- 促进中国情绪分析模型的开发和评估.
- 为自然语言处理研究人员提供可靠的资源.
主要方法:
- 从22个不同领域收集了超过20万条社交媒体评论.
- 预先处理的数据,保留情绪解释所必需的表情符号.
- 每个评论都由研究人员手动标注情绪标签 (积极,消极,中立).
- 按主题分组评论进行结构化分析.
主要成果:
- 一个大规模的,手动注释的情绪分析数据集,用于中国社交媒体.
- 数据集涵盖了22个不同主题,用户参与度很高.
- 确保情绪分析模型的培训,验证和测试的可靠性.
结论:
- 引入的数据集是推动中国情绪分析的宝贵资源.
- 它支持创建更准确,更强大的自然语言处理模型.
- 能够更深入地了解中国社交媒体上公众论的表达.
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