Dual Sentiment-aware Networks for Adverse Drug Reactions Detection

Adverse drug reactions (ADRs) are harmful reactions that occur with qualified drugs under normal dosage. Due to its significant impact on patients’ health, timely detection for ADRs is of great significance. Current approaches typically introduce sentiment to enhance the detection of ADRs because sentiment information can reflect the subjective feelings of the patient about the drug. However, none of the existing works has considered both explicit and implicit sentiment knowledge. To this end, we propose a novel approach named DSN to adaptively fuse explicit and implicit sentiment knowledge for adverse drug reactions detection. Specifically, for explicit sentiment knowledge, we first propose a dimension extension approach to avoid the information loss problem, and then utilize an aware network based on attention mechanism to obtain a textual representation that incorporates explicit sentiment knowledge; As to implicit sentiment knowledge, we design a fusion network based on a gating mechanism to adaptively learn text and implicit sentiment knowledge in order to help and guide the deeper fusion of text and two kinds of sentiment knowledge. Extensive experiments conducted on TwiMed_Twitter dataset demonstrate the superiority of our proposed DSN over existing methods.