用于情绪分析和刺言论检测的注释数据集:公共安全领域的双语代码混合英语-马来语社交媒体数据
Mohd Suhairi Md Suhaimin1,2, Mohd Hanafi Ahmad Hijazi1,3, Ervin Gubin Moung1
1Data Technology and Applications Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia.
这项研究引入了用于公共安全情绪分析的新数据集,解决了刺和代码混合语言等挑战. 该数据集有助于在多语言地区的自然语言处理和机器学习.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 对公共安全的情绪分析对于了解公众对事件和危机的看法至关重要.
- 刺和双语代码混合内容对现有的情绪分析系统构成重大挑战.
- 专业数据集的稀缺性阻碍了该领域的研究和开发.
研究的目的:
- 为情绪分析引入一个全面的,系统地获取和注释的数据集.
- 通过关注刺和代码混合内容来解决当前数据集的局限性.
- 为公共安全领域支持自然语言处理和机器学习方面的进步.
主要方法:
- 数据采集涉及关键词搜索,查询和从社交媒体平台上取数据.
- 标注包括数据合并,选择和标记,由三个专家标注者对情绪和刺进行标注.
- 语言识别由文学专家进行.
主要成果:
- 一个专门为公共安全情绪分析而设计的新的注释数据集已被创建.
- 该数据集包含细微的语言特征,如刺和代码混合.
- 专家注释确保了情感,刺和语言识别的高质量标签.
结论:
- 开发的数据集是推进公共安全情绪分析的宝贵资源.
- 它特别有利于在多语言环境中的研究,特别是在东南亚.
- 这一资源将有助于开发更强大,更准确的情绪分析模型.
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