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Sentiment Polarity Detection in Social Networks: An Approach for Asthma Disease Management

机译:社交网络中情调极性检测:哮喘病管理方法

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摘要

Asthma disease is a serious health problem that affects all age groups. Asthma-related hospitalizations and deaths have declined in some countries. However, the number of patients with symptoms has increased in the last years. Even though asthma patients have contact with health professionals, they must be an active part in treatment team. On the other hand, there has been an exponential growth of information about healthcare and diseases management on social networks such as Twitter. Aiming to benefit from this information, in this work we propose a method for detecting the emotional reaction of patients about asthma domain concepts such as physical activities, drugs, among others. The findings obtained from the analysis of such information can help to other patients to avoid habits that could harm their health. Our proposal was evaluated with a corpus of Twitter messages obtaining a precision of 82.95%, a recall of 82.27%, and F-measure of 82.36% in sentiment polarity identification.
机译:哮喘病是一种严重的健康问题,影响所有年龄段。有关的哮喘相关住院和死亡在一些国家下降。然而,过去几年症状患者的数量增加。尽管哮喘患者与卫生专业人士接触,但它们必须是治疗团队的活跃部分。另一方面,关于在Twitter等社交网络上的医疗保健和疾病管理的信息的指数增长。旨在从这些信息中受益,在这项工作中,我们提出了一种检测患者哮喘域概念等体育活动,药物等哮喘域概念的情绪反应的方法。从这些信息的分析中获得的结果可以帮助其他患者避免可能损害健康的习惯。我们的提案是用Twitter消息的语料库评估,获得了82.95%的精确度,召回的82.27%,F-Feat度为82.36%,在情绪极性识别。

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