Abstract
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.
Original language | English |
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Pages (from-to) | 83-86 |
Number of pages | 4 |
Journal | CEUR Workshop Proceedings |
Volume | 1996 |
Publication status | Published - Jan 1 2017 |
Event | 2nd Social Media Mining for Health Research and Applications Workshop, SMM4H 2017 - Washington, United States Duration: Nov 4 2017 → … |
ASJC Scopus subject areas
- General Computer Science