Sofean Mustafa, Smith Matthew
University of Hannover, Distributed Computing & Security Group, Germany.
Stud Health Technol Inform. 2013;192:1118.
Online social networks play a vital role in daily life to share the opinions or behaviors on different topics. The data of social networks can be used to understand health-related behaviors. In this work, we used Twitter status updates to survey of smoking behaviors among the users. We introduce approach to classify the sentiment of smoke-related tweets into positive and negative tweets. The classifier is based on the Support Vector Machines (SVMs) and can achieve high accuracy up to 86%.
在线社交网络在日常生活中发挥着至关重要的作用，用于分享关于不同话题的观点或行为。社交网络的数据可用于了解与健康相关的行为。在这项工作中，我们使用推特状态更新来调查用户中的吸烟行为。我们引入了一种方法，将与吸烟相关的推文情感分类为正面和负面推文。该分类器基于支持向量机（SVM），可以达到高达86%的高精度。
