SentiUrdu-1M:一个大规模的推特数据集用于乌尔都文本情绪分析,使用弱监督学习
Abdul Ghafoor1, Ali Shariq Imran2, Sher Muhammad Daudpota1
1Dept. of Computer Science, Sukkur IBA University, Sukkur, Pakistan.
PloS one
|August 30, 2023
概括
研究人员创建了第一个大规模的乌尔都语推特数据集,用于情绪分析. 使用表情符号和SentiWordNet的新弱监督方法,准确地标记推特,优于VADER和TextBlob等现有工具.
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
- 人工智能的人工智能
背景情况:
- 乌尔都语由2.3亿多人使用,是一种资源较低的语言,缺乏大规模的数据集用于研究.
- 深度学习和预训练的词嵌入凸显了对资源不足的语言更多数据的需求.
- 标记数据的稀缺性阻碍了乌尔都语情感分析和情感识别方面的有意义研究.
研究的目的:
- 通过创建第一个大规模的乌尔都语推特数据集来解决乌尔都语语言处理的数据短缺问题.
- 开发和评估用于情绪分析的自动推文标签的弱监督方法.
- 将拟议的标签方法的有效性与VADER和TextBlob等既有工具进行比较.
主要方法:
- 收集了1,140,821个乌尔都语推特的数据集.
- 开发了一种弱监督的标签方法,利用表情符号和SentiWordNet进行自动情绪分类 (积极,消极,中立).
- 实施了基线深度学习模型,以评估与VADER和TextBlob.com相比拟的方法的准确性.
主要成果:
- 提出的弱监督方法证明了乌尔都语推文的有效自动标签.
- VADER和TextBlob主要将推特分类为中立,显示高相关性,可能是因为他们无法解释表情符号.
- 新的数据集和标签方法为乌尔都语情感分析和情感识别研究提供了宝贵的资源.
结论:
- 创建一个大规模的乌尔都语推文数据集是对低资源语言处理的重要贡献.
- 拟议的弱监督的标签方法为乌尔都语情绪分析提供了一个可行的和准确的替代方案.
- 未来的研究可以利用这个数据集和方法来完成高级的乌尔都语NLP任务,包括情感识别.
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