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Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017Workshop on Social Media Mining for Health Applications (SMM4H): Task 1 -classification of tweets mentioning adverse drug reactions, and Task 2 -classification of tweets describing personal medication intake. For both tasks,we trained Support Vector Machine classifiers using a variety of surface-form,sentiment, and domain-specific features. With nine teams participating in eachtask, our submissions ranked first on Task 1 and third on Task 2. Handlingconsiderable class imbalance proved crucial for Task 1. We applied anunder-sampling technique to reduce class imbalance (from about 1:10 to 1:2).Standard n-gram features, n-grams generalized over domain terms, as well asgeneral-domain and domain-specific word embeddings had a substantial impact onthe overall performance in both tasks. On the other hand, including sentimentlexicon features did not result in any improvement.

NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake