TY - JOUR AU - Myslín, Mark AU - Zhu, Shu-Hong AU - Chapman, Wendy AU - Conway, Mike PY - 2013 DA - 2013/08/29 TI -使用Twitter来检查吸烟行为和对新兴烟草产品的看法JO - J Med Internet Res SP - e174 VL - 15 IS - 8 KW -社交媒体KW -推特消息KW -吸烟KW -自然语言处理AB -背景:Twitter等社交媒体平台正迅速成为公共卫生监测应用程序的关键资源，但Twitter用户对烟草的知情程度和情绪知之甚少，尤其是水烟和电子烟带来的新烟草控制挑战。目的:开发与烟草相关的Twitter帖子的内容和情感分析，并构建机器学习分类器来检测与烟草相关的帖子和对烟草的情感，特别关注像水烟和电子烟这样的新兴产品。方法:从2011年12月到2012年7月，我们每隔15天收集7362条与烟草相关的推特帖子。每条推文都使用三轴模式手动分类，捕捉类型、主题和情绪。使用收集到的数据，机器学习分类器被训练来检测烟草相关与不相关的推文，以及积极与消极的情绪，使用Naïve贝叶斯，k-nearest neighbors和支持向量机(SVM)算法。最后，计算每个类别之间的意外系数，以发现紧急模式。结果:最普遍的类型是第一次和二手经验和意见，最常见的主题是水烟，戒烟和快乐。在提到烟草的推文中，对烟草的态度总体上更积极(1939/4215,46%的推文)，而不是消极(1349/4215,32%)或中立，甚至不包括被归类为营销的9%的推文。三个独立的指标融合在一起，支持了一种新兴的区分，一方面，水烟和电子烟对应着积极的情绪，另一方面，传统烟草产品和更普遍的参考文献对应着消极的情绪。 These metrics included correlations between categories in the annotation scheme (phihookah-positive=0.39; phie-cigs-positive=0.19); correlations between search keywords and sentiment (χ24=414.50, P<.001, Cramer’s V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85). Conclusions: Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications. SN - 14388871 UR - //www.mybigtv.com/2013/8/e174/ UR - https://doi.org/10.2196/jmir.2534 UR - http://www.ncbi.nlm.nih.gov/pubmed/23989137 DO - 10.2196/jmir.2534 ID - info:doi/10.2196/jmir.2534 ER -