用情绪,情绪和情绪来检测人类行为的数据集构建 罗马乌尔都语 罗马乌尔都语
Asia Samreen1,2, Syed Asif Ali2
1Department of Computer Science, Bahria University, Karachi Campus, 13 National Stadium Road, Karachi 75260, Pakistan.
Data in brief
|February 2, 2024
概括
这项研究解决了在罗马乌尔都语和英语社交媒体文本中理解情绪的挑战. 它介绍了一个新数据集,包含超过14000个罗马乌尔都语情感词典,用于改进情感分析.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 情绪分析 情绪分析
背景情况:
- 混合语言的使用 (罗马乌尔都语和英语) 在社交媒体沟通中很普遍.
- 在罗马乌尔都语中缺乏标准化的拼写,这给准确的情感解释带来了挑战.
- 现有的情感词典需要适应这种混合语言背景.
研究的目的:
- 开发一个全面的数据集的罗马乌尔都语情感词典.
- 为了方便在混合代码的社交媒体文本中准确分析情绪和情绪.
- 改进在线互动中对用户情绪的计算理解.
主要方法:
- 使用Python脚本将现有的乌尔都语情感词典 (NRC) 翻译成罗马乌尔都语.
- 使用三个在线乌尔都语字典验证翻译准确性.
- 使用 unigram 特性和字符串/词典距离估计,对文本数据进行注释.
主要成果:
- 创建一个数据集,包含超过14000个罗马乌尔都语情感词典,每个词典都有9种不同的情感和极性.
- 从基线样本中自动注释大约1万个句子.
- 提供情绪和情绪信息以及情绪数据.
结论:
- 开发的罗马乌尔都语情感词典数据集是混合代码社交媒体情感分析的宝贵资源.
- 该方法提供了一个强大的方法,用于为低资源语言创建和注释情感词典.
- 这项工作有助于更好地理解数字通信中的情绪表达.
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