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Sentiment Analysis on Smoking in Social Networks
Mustafa Sofean, Matthew Smith
1118
10.3233/978-1-61499-289-9-1118
Studies in Health Technology and Informatics
Volume 192: MEDINFO 2013
Abstract

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%.

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