In this study, we describe our methods to automatically classify Twitter posts describing events of adverse drug reaction and medication intake. We developed classifiers using linear support vector machines (SVM) and Naïve Bayes Multinomial (NBM) models. We extracted features to develop our models and conducted experiments to examine their effectiveness as part of our participation in AMIA 2017 Social Media Mining for Health Applications shared task. For both tasks, the best-performed models on the test sets were trained by using NBM with n-gram, part-of-speech and lexicon features, which achieved F-scores of 0.295 and 0.615, respectively.
|Number of pages||4|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - Jan 1 2017|
ASJC Scopus subject areas
- Computer Science(all)