Sentiment analysis on smoking in social networks.
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%.
University of Hannover, Distributed Computing & Security Group, Germany.
Mustafa
M
Sofean
Matthew
M
Smith
D001185:Artificial Intelligence; D001294:Attitude to Health; D057225:Data Mining / Q000379:methods*; D015438:Health Behavior; D010363:Pattern Recognition, Automated / Q000379:methods; D011639:Public Opinion; D012907:Smoking / Q000453:epidemiology*; D061108:Social Media / Q000706:statistics & numerical data*; D012944:Social Support; D018875:Vocabulary, Controlled