Machine Learning based Emotion classification in the COVID-19 Real World Worry Dataset

Authors : Hakan ÇAKAR, Abdulkadir SENGUR

Pages : 24-31

View : 13 | Download : 6

Publication Date : 2021-03-01

Article Type : Research Paper

Abstract :COVID-19 pandemic has a dramatic impact on economies and communities all around the world. With social distancing in place and various measures of lockdowns, it becomes significant to understand emotional responses on a great scale. In this paper, a study is presented that determines human emotions during COVID-19 using various machine learning insert ignore into journalissuearticles values(ML); approaches. To this end, various techniques such as Decision Trees insert ignore into journalissuearticles values(DT);, Support Vector Machines insert ignore into journalissuearticles values(SVM);, k-nearest neighbor insert ignore into journalissuearticles values(k-NN);, Neural Networks insert ignore into journalissuearticles values(NN); and Naïve Bayes insert ignore into journalissuearticles values(NB); methods are used in determination of the human emotions. The mentioned techniques are used on a dataset namely Real World Worry dataset insert ignore into journalissuearticles values(RWWD); that was collected during COVID-19. The dataset, which covers eight emotions on a 9-point scale, grading their anxiety levels about the COVID-19 situation, was collected by using 2500 participants. The performance evaluation of the ML techniques on emotion prediction is carried out by using the accuracy score. Five-fold cross validation technique is also adopted in experiments. The experiment works show that the ML approaches are promising in determining the emotion in COVID-19 RWWD. More specifically, the NN method produced the highest average accuracy scores for both emotion and gender classification where a 75.7% and 72.1% average scores were obtained.
Keywords : COVID 19 worry dataset, emotion classification, machine learning