Research Article | OPEN ACCESS
Drug Side Effect Detection as Implicit Opinion from Medical Reviews (Research Article)
Monireh Ebrahimi, Amir Hossein Yazdavar, Naomie Binti Salim, Shirin Noekhah and Deborah Libu Paris
Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2016 11:1086-1094
Received: October 9, 2015 | Accepted: November 4, 2015 | Published: June 05, 2016
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.
Keywords:
Drug reviews, implicit opinion, rule-based method, side effect, SVM algorithm,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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