Data-Driven Solution to Identify Sentiments from Online Drug Reviews

• Published in: Computers (Basel) Vol. 12; no. 4; p. 87
• Main Authors: Haque, Rezaul, Laskar, Saddam Hossain, Khushbu, Katura Gania, Hasan, Md Junayed, Uddin, Jia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Algorithms; Bi-LSTM; Classification; Classifiers; Complications and side effects; Computational linguistics; Customer feedback; Data mining; Decision making; Decision trees; Deep learning; Dietary supplements; drug discovery; drug sentiment analysis; Drugs; Electronic commerce; Embedding; GloVe; Health care reform; Information sources; Language processing; Machine learning; Marketing; Medical advice systems; Natural language interfaces; Natural language processing; Product development; Sentiment analysis; Social networks; User generated content; word embedding; Taiwan
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers12040087

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial adverse drug reactions, and machine learning (ML) approaches such as sentiment analysis (SA) can be employed to derive valuable insights. However, given the sheer volume of comments, it is often impractical for consumers to manually review all of them before determining a purchase decision. Therefore, drug assessments can serve as a valuable source of medical information for both healthcare professionals and the general public, aiding in decision making and improving public monitoring systems by revealing collective experiences. Nonetheless, the unstructured and linguistic nature of the comments poses a significant challenge for effective categorization, with previous studies having utilized machine and deep learning (DL) algorithms to address this challenge. Despite both approaches showing promising results, DL classifiers outperformed ML classifiers in previous studies. Therefore, the objective of our study was to improve upon earlier research by applying SA to medication reviews and training five ML algorithms on two distinct feature extractions and four DL classifiers on two different word-embedding approaches to obtain higher categorization scores. Our findings indicated that the random forest trained on the count vectorizer outperformed all other ML algorithms, achieving an accuracy and F1 score of 96.65% and 96.42%, respectively. Furthermore, the bidirectional LSTM (Bi-LSTM) model trained on GloVe embedding resulted in an even better accuracy and F1 score, reaching 97.40% and 97.42%, respectively. Hence, by utilizing appropriate natural language processing and ML algorithms, we were able to achieve superior results compared to earlier studies.