从2022年5月1日至9月5日,评估基于词典的模型的自动注释,用于M-pox推文的姿态检测
Nicholas Perikli1,2,3, Srimoy Bhattacharya1,3, Blessing Ogbuokiri3,4,5
1School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
PLOS digital health
|July 30, 2024
概括
来自VADER和TextBlob的自动姿势检测标签对于训练NLP模型是不准确的. 在手动标记的数据上微调变压器模型显著提高了定位检测的准确性,特别是在与健康相关的环境中.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 计算社会科学 计算社会科学
背景情况:
- 基于词典的模型,如VADER和TextBlob,为监督学习提供自动数据标签,但它们对于姿势检测的准确性受到争议.
- 在以前的研究中,尽管自动立场标签经常使用,但其评估不足.
研究的目的:
- 评估VADER和TextBlob用于自动定位检测标签的准确性.
- 评估基于微调变压器的模型对用于定位检测的手动标记数据的影响.
主要方法:
- 手动标记了一个Twitter (X) 数据集用于M-pox姿势检测.
- 在手动标记的数据集上微调各种基于变压器的模型.
- 将微调模型的精度与VADER和TextBlob的自动标签进行了比较.
主要成果:
- 微调模型的精度比VADER提高了38%,比TextBlob提高了72.5%.
- 主题建模显示,微调减少了对特定子主题的错误分类.
- 发现自动姿势检测标签对于敏感的,与健康相关的主题是不可靠的.
结论:
- 在手动标记的数据上微调变压器模型显著提高了位置检测的准确性.
- 手动数据标签对于开发强大的NLP模型来分析健康危机期间的社交媒体至关重要.
- 基于词典的模型的自动标签对于关键领域的高精度立场分析是不够的.
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