Aspect-Based Opinion Mining in Drug Reviews

Aspect-based opinion mining can be applied to extract relevant information expressed by patients in drug reviews (e.g., adverse reactions, efficacy of a drug, symptoms and conditions of patients). This new domain of application presents challenges as well as opportunities for research in opinion mining. Nevertheless, the literature is still scarce of methods to extract multiple relevant aspects present in drug reviews. In this paper we propose a method to extract and classify aspects in drug reviews. The proposed solution has two main steps. In the aspect extraction, a method based on syntactic dependency paths is proposed to extract opinion pairs in drug reviews, composed by an aspect term associated to a sentiment modifier. In the aspect classification, a supervised classification is proposed based on domain and linguistics resources to classify the opinion pairs by aspect type (e.g., condition, adverse reaction, dosage and effectiveness). In order to evaluate the proposed method we conducted experiments with datasets related to three different diseases: ADHD, AIDS and Anxiety. Promising results were obtained in the experiments and various issues were identified and discussed.