Aspect extraction and classification for sentiment analysis in drug reviews

Aspect-based sentiment analysis (ABSA) of patients' opinions expressed in drug reviews can extract valuable information about specific aspects of a particular drug such as effectiveness, side effects and patient conditions. One of the most important and challenging tasks of ABSA is to extract the implicit and explicit aspects from a text, and to classify the extracted aspects into predetermined classes. Supervised learning algorithms possess high accuracy in extracting and classifying aspects; however, they require annotated datasets whose manual construction is time-consuming and costly. In this paper, first a new method was introduced for identifying expressions that indicate an aspect in user reviews about drugs in English. Then, distant supervision was adopted to automate the construction of a training set using sentences and phrases that are annotated as aspect classes in the drug domain. The results of the experiments showed that the proposed method is able to identify various aspects of the test set with 74.4% F-measure, and outperforms the existing aspect extraction methods. Also, training the random forest classifier on the dataset that was constructed via distant supervision obtained the F-measure of 73.96%, and employing this dataset to fine-tune BERT for aspect classification yielded better F-measure (78.05%) in comparison to an existing method in which the random forest classifier trained on an accurate manually constructed dataset.