@文章{信息:doi/10.2196/10043，作者=“Sewalk, Kara C和Tuli, Gaurav和Hswen, Yulin和Brownstein, John S和Hawkins, Jared B”，标题=“使用Twitter来检查基于web的美国患者体验情绪:纵向研究”，期刊=“J医学互联网研究”，年=“2018”，月=“10月”，日=“12”，卷=“20”，数=“10”，页=“e10043”，关键词=“医疗保健;社交媒体;背景:美国各地在获得医疗保健方面存在有据可查的差异。先前的研究表明，基于网络的关于患者体验和医疗保健意见的数据可以从Twitter上获得。对Twitter数据的情感分析可以用来研究美国各地患者对医疗保健的看法差异。目的:我们研究的目的是在四年的时间里，提供美国各地推特上患者体验情绪的特征描述。方法:使用来自Twitter的数据，我们开发了一套4个软件组件来自动标记和检查讨论患者体验的推文数据库。该集合包括一个用于确定患者体验推文的分类器、一个用于社交数据的地理位置推断引擎、一个改进的情感分类器和一个用于确定推文是来自美国大都市还是非大都市地区的引擎。利用检索到的信息，我们对国家和地区层面的推文情绪进行了时空检查。我们研究了推文发布时的一天时间和一周时间趋势。 Statistical analyses were conducted to determine if any differences existed between the discussions of patient experience in metropolitan and nonmetropolitan areas. Results: We collected 27.3 million tweets between February 1, 2013 and February 28, 2017, using a set of patient experience-related keywords; the classifier was able to identify 2,759,257 tweets labeled as patient experience. We identified the approximate location of 31.76{\%} (876,384/2,759,257) patient experience tweets using a geolocation classifier to conduct spatial analyses. At the national level, we observed 27.83{\%} (243,903/876,384) positive patient experience tweets, 36.22{\%} (317,445/876,384) neutral patient experience tweets, and 35.95{\%} (315,036/876,384) negative patient experience tweets. There were slight differences in tweet sentiments across all regions of the United States during the 4-year study period. We found the average sentiment polarity shifted toward less negative over the study period across all the regions of the United States. We observed the sentiment of tweets to have a lower negative fraction during daytime hours, whereas the sentiment of tweets posted between 8 pm and 10 am had a higher negative fraction. Nationally, sentiment scores for tweets in metropolitan areas were found to be more extremely negative and mildly positive compared with tweets in nonmetropolitan areas. This result is statistically significant (P<.001). Tweets with extremely negative sentiments had a medium effect size (d=0.34) at the national level. Conclusions: This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States. ", issn="1438-8871", doi="10.2196/10043", url="//www.mybigtv.com/2018/10/e10043/", url="https://doi.org/10.2196/10043", url="http://www.ncbi.nlm.nih.gov/pubmed/30314959" }