The evaluation of Parkinson's disease with sentiment analysis using deep learning methods and word embedding models

Abstract

Parkinson's disease is a common neurodegenerative neurological disorder, which affects the patient's quality of life, has significant social and economic effects, and is difficult to diagnose early due to the gradual appearance of symptoms. Examining the discussion of Parkinson's disease in social media platforms such as Twitter provides a platform where patients communicate each other in both diagnosis and treatment stage of the Parkinson's disease. The purpose of this work is to evaluate and compare the sentiment analysis of people about Parkinson's disease by using deep learning and word embedding models. To the best of our knowledge, this is the very first study to analyze Parkinson's disease through social media by using word embedding models and deep learning algorithms. In this study, Word2Vec, GloVe, and FastText as word embedding models and Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short Term Memory Networks (LSTMs) as deep learning techniques are blended and used for classification purpose. Extensive experiments are conducted to analyze the sentiments of user comments about Parkinson's disease using word embedding models and deep learning algorithms on English Twitter dataset. The remarkable classification success with 75.12% of accuracy is observed in the experiments through the result of blending Word2Vec as a word embedding model and CNN as a deep learning technique. This study demonstrates the effectiveness of using word embedding models and deep learning algorithms to understand patients' needs, and provides a valuable contribution to the treatment process by analyzing the feelings of Parkinson's patients and their relatives through social media.