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Dergiler / Bilgisayar Bilimleri / 2026 / Cilt: 6 Sayı: 1

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

Hakan Çakar(FIRAT UNIVERSITY, FACULTY OF TECHNOLOGY)0000-0002-4918-9401
Abdulkadir Sengur(FIRAT UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING)0000-0002-7365-4318
Sayfa
24–31
Yayın Tarihi
2021-03-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:COVID-19 pandemic has a dramatic impact on economies and communities all around the world.
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Temel Bulgular & Veri:With social distancing in place and various measures of lockdowns, it becomes significant to understand emotional responses on a great scale.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:COVID-19 worry dataset · emotion classification · machine learning

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 (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.

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Bu Makaleye Atıf Yap

Çakar, H., Sengur, A. (2021). Machine Learning based Emotion classification in the COVID-19 Real World Worry Dataset. Bilgisayar Bilimleri, Cilt: 6 Sayı: 1, 24–31. https://ajindex.com/articles/1464337

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