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This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non\ue4f8health related concerns in relations to the corpus of negative sentiments regarding diet, diabetes, exercise and obesity (DDEO). Through the collection of six million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts and public health professionals to collectively address DDEO\ue4f8related issues.<\/jats:p>","DOI":"10.1002\/pra2.2017.14505401039","type":"journal-article","created":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T03:35:36Z","timestamp":1508816136000},"page":"357-365","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise"],"prefix":"10.1002","volume":"54","author":[{"suffix":"Jr.","given":"George","family":"Shaw","sequence":"first","affiliation":[{"name":"School of Library and Information Science University of South Carolina  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Karami","sequence":"additional","affiliation":[{"name":"School of Library and Information Science University of South Carolina  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,10,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"Abbar S. 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