Citation

Azam N, Ahmad T, Haq NU. PeerJ Comput. Sci. 2021; 7.

Copyright

(Copyright © 2021, PeerJ)

DOI

10.7717/PEERJ-CS.751

PMID

unavailable

Abstract

Human feelings are fundamental to perceive the conduct and state of mind of an individual. A healthy emotional state is one significant highlight to improve personal satisfaction. On the other hand, bad emotional health can prompt social or psychological well-being issues. Recognizing or detecting feelings in online health care data gives important and helpful information regarding the emotional state of patients. To recognize or detection of patient's emotion against a specific disease using text from online sources is a challenging task. In this paper, we propose a method for the automatic detection of patient's emotions in healthcare data using supervised machine learning approaches. For this purpose, we created a new dataset named EmoHD, comprising of 4,202 text samples against eight disease classes and six emotion classes, gathered from different online resources. We used six different supervised machine learning models based on different feature engineering techniques. We also performed a detailed comparison of the chosen six machine learning algorithms using different feature vectors on our dataset. We achieved the highest 87% accuracy using MultiLayer Perceptron as compared to other state of the art models. Moreover, we use the emotional guidance scale to show that there is a link between negative emotion and psychological health issues. Our proposed work will be helpful to automatically detect a patient's emotion during disease and to avoid extreme acts like suicide, mental disorders, or psychological health issues. The implementation details are made publicly available at the given link: https://bit.ly/2NQeGET. © 2021 Azam et al. All Rights Reserved.

Language: en

Keywords

Artificial Intelligence; Health care; Data mining; Learning algorithms; Emotional state; Natural languages; Machine-learning; Character recognition; Data mining and machine learning; Data Mining and Machine Learning; Emotion detection; Emotion guidance scale; Natural Language and Speech; Natural speech; Negative emotion mapping; Negative emotions mapping; Patient’s emotion; Supervised learning; Supervised machine learning