Evaluation of Ensemble Algorithms and Deep Learning Transformers in Medical Sentiment Prediction

Abstract

Social media continuously produces digital information that can be used to improve service quality. In this aspect sentiment prediction, automated analysis of written user reviews, is an important research area from service quality point of view. Online sentiment prediction is a rich research area from e-business perspective. However, identification of sentiment from medical service user reviews is particularly researched less frequently. From Turkish language point of view, the medical informatics literature needs more research to design automated medical sentiment systems. Automated sentiment analysis systems particularly make use of Machine Learning (ML) algorithm in tandem with Natural Language Processing (NLP) methods to address written user reviews. In this work, ensemble learning approaches are compared with newly developed deep learning variations, Bidirectional Encoder Representations from Transformers (BERT), to investigate medical sentiments. As the obtained results are evaluated, it is observed that newly proposed transformer models are perfectly successful to identify sentiment of Turkish medical reviews.