Adaptive E-learning content generation based on semantic web technology

Abstract

Individuals increasingly rely on social media to discuss health-related issues. One way to provide easier access to relevant in- formation is through sentiment analysis - classifying text into polarity classes such as positive and negative. In this paper, we generated freely available datasets of WebMD.com drug reviews and star ratings for <i>Common</i>, <i>Cancer</i>, <i>Depression</i>, <i>Diabetes</i>, and <i>Hypertension</i> drugs. We explored four supervised learning models: Naive Bayes, Random Forests, Support Vector Machines, and Convolutional Neural Networks for the purpose of determining the polarity of drug reviews. We conducted inter-domain and cross-domain evaluations. We found that SVM obtained the highest f-measure on average and that cross-domain training produced similar or higher results to models trained directly on their respective datasets.