Publication

Abstract : The advancements in information and communication technologies contributed greatly to the development of social media and other platforms where people express their opinions and experiences. There are several platforms such as drugs.com where people rate pharmaceutical drugs and also give comments and reviews on the drugs they use and their side effects. It is important to analyze such reviews to find out the sentiment, opinions, drug efficacy, and most importantly, adverse drug reactions. Health mention classification deals with classifying such user-generated text into different classes of health mentions such as obesity, anxiety, and more. This work uses machine learning approaches for classifying health mentions from the publicly available health-mention dataset. Both the shallow machine learning algorithms and deep learning approaches with pre-trained embeddings have been implemented and the performances were compared with respect to the precision, recall, and f1-score. The experimental results show that machine learning approaches will be a good choice for automatically classifying health mentions from the large amount of user-generated drug reviews that may help different stakeholders of the healthcare industry to better understand the market and consumers.