Binary Classification of Twitter Posts for Adverse Drug Reactions
نویسندگان
چکیده
Nowadays, social media is often being used by users to create public messages or posts that are related to their health. With the increasing number of social media usage, a trend has been observed of users creating posts related to adverse drug reactions. Mining social media data for these information can be used for pharmacological post-marketing surveillance and monitoring. We developed a binary classifier using linear support vector machines to automatically classify Twitter posts assertive of adverse drug reactions. Two runs were devised to evaluate the classifier. Our classifier achieved an F-score of 0.29 and 0.33 for the first and second run respectively.
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