Drug Side Effect Detection as Implicit Opinion from Medical Reviews (Research Article)

Abstract

The enormous growth of online reviews in social media provides a valuable resource for human decision-making activities in diverse domains such as the medical domain. Extracting explicit and implicit opinions is one of the main tasks in the opinion mining area. As implicit opinion mining is a complicated task, limited work has been done on it, especially in the medical domain, as implicit opinion is a domain dependent task. Side effects are one of the critical concepts the recognition of which is a challenging task since it coincides with disease symptoms both lexically and syntactically. To the best of our knowledge, limited work has been done on side effect extraction from drug reviews. This study tries to extract drug side effects as implicit opinions from drug reviews of drugratingz.com by using the rule-based and SVM techniques. Due to the novelty of this issue, corpus construction is also carried out. The results proved that the combination of lexical, syntactical, contextual and semantic features leads to the best results in the SVM technique in comparison with the rule-based algorithm in terms of side effect detection. In this study, we develop a system to detect side effects in the drug reviews as a subtask of detecting implicit opinions in medical sources and discriminate between side effects and disease symptoms. The proposed technique, as an implicit opinion mining system, can help patients to investigate the drug before taking it and help physicians and drug producers to consider user feedback in their decision-making.