TY - JOUR
T1 - NTTMU system in the 2nd social media mining for health applications shared task
AU - Wang, Chen Kai
AU - Chang, Nai Wun
AU - Su, Emily Chia Yu
AU - Dai, Hong Jie
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85037031509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037031509&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85037031509
SN - 1613-0073
VL - 1996
SP - 83
EP - 86
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2nd Social Media Mining for Health Research and Applications Workshop, SMM4H 2017
Y2 - 4 November 2017
ER -