Medicinal Side-Effect Analysis Using Twitter Feed

As the use of social media network has been increasing, people tend to share health-related information on social sites. Twitter is used by large number of users and it is a wide source of information to analyze the drug related side effect. In this paper, we have developed an approach to analyze the contents of tweets to identify the adverse effects of a drug. An annotated dataset is used to train SVM classifier to identify the tweets showcasing medicinal side effects. The use of feature selection and dimensionality reduction techniques have allowed us to enhance the performance of the classifier in terms of accuracy by 10.34% as well as efficiency by nearly 66.31% as compared to the previous similar approaches.