@文章{信息:doi/10.2196/29584，作者=“Kummervold, Per E和Martin, Sam和Dada, Sara和Kilich, Eliz和Denny, Chermain和Paterson, Pauline和Larson, Heidi J”，标题=“用基于transformer的机器学习模型对疫苗信心进行分类:Twitter话语中疫苗情绪的差异分析”，期刊=“JMIR Med Inform”，年=“2021”，月=“10”，日=“8”，卷=“9”，数=“10”，页=“e29584”，关键词=“计算机科学;信息技术;公共卫生;卫生人文;疫苗;背景:社交媒体已经成为个人讨论和辩论各种主题的既定平台，包括疫苗接种。随着网络上的对话越来越多，而产妇疫苗接种率低于预期，这些对话可以为未来的干预措施提供有用的见解。然而，由于网络帖子的数量，手动注释和分析是困难和耗时的。这类分析的自动化过程(如自然语言处理)在从大量文本中提取复杂立场(如对疫苗接种的态度)方面面临挑战。目的:本研究的目的是建立在基于转座子的机器学习方法的最新进展基础上，并测试基于变压器的机器学习是否可以用作评估社交媒体帖子中对怀孕期间接种疫苗表达立场的工具。 Methods: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. Results: We found the accuracy of the machine learning techniques to be 81.8{\%} (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3{\%}, 77.9{\%}, and 77.5{\%}. Conclusions: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions. ", issn="2291-9694", doi="10.2196/29584", url="https://medinform.www.mybigtv.com/2021/10/e29584", url="https://doi.org/10.2196/29584", url="http://www.ncbi.nlm.nih.gov/pubmed/34623312" }