Yıl 2021, Cilt 6 , Sayı 1, Sayfalar 24 - 31 2021-03-01

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

Hakan ÇAKAR [1] , Abdulkadir SENGUR [2]


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 (ML) approaches. To this end, various techniques such as Decision Trees (DT), Support Vector Machines (SVM), k-nearest neighbor (k-NN), Neural Networks (NN) and Naïve Bayes (NB) methods are used in determination of the human emotions. The mentioned techniques are used on a dataset namely Real World Worry dataset (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.
COVID-19 worry dataset, emotion classification, machine learning
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Yazarlar

Orcid: 0000-0002-4918-9401
Yazar: Hakan ÇAKAR (Sorumlu Yazar)
Kurum: FIRAT UNIVERSITY, FACULTY OF TECHNOLOGY
Ülke: Turkey


Orcid: 0000-0002-7365-4318
Yazar: Abdulkadir SENGUR
Kurum: FIRAT UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING
Ülke: Turkey


Tarihler

Başvuru Tarihi : 27 Eylül 2020
Kabul Tarihi : 1 Ocak 2021
Yayımlanma Tarihi : 1 Mart 2021

APA Çakar, H , Sengur, A . (2021). Machine Learning based Emotion classification in the COVID-19 Real World Worry Dataset . Computer Science , 6 (1) , 24-31 . Retrieved from https://dergipark.org.tr/tr/pub/bbd/issue/59753/800761