BERT-based Transfer Learning Model for COVID-19 Sentiment Analysis on Turkish Instagram Comments


  • Habibe Karayiğit Department of Electrical and Electronics Engineering, Mersin University,
  • Ali Akdagli Department of Electrical and Electronics Engineering, Mersin University, 33343, Turkey
  • Çiğdem İnan Acı Department of Computer Engineering, Mersin University, 33343, Turkey



Social Media, COVID-19, Sentiment Analysis, Deep Learning, BERT, Transfer Learning


First seen in Wuhan, China, the coronavirus disease (COVID-19) became a worldwide epidemic. Turkey’s first reported case was announced on March 11, 2020—the day the World Health Organization declared COVID-19 is a pandemic. Due to the intense and widespread use of social media during the pandemic, determining the role and effect (i.e., positive, negative, neutral) of social media gives us important information about society's perspective on events. In our study, two datasets (i.e. Dataset1, Dataset2) consisting of Instagram comments on COVID-19 were composed between different dates of the pandemic, and the change between users' feelings and thoughts about the epidemic was analyzed. The datasets are the first publicly available Turkish datasets on the sentiment analysis of COVID-19, as far as we know. The sentiment analysis of Turkish Instagram comments was performed using Machine Learning models (i.e., Traditional Machine Learning, Deep Learning, and BERT-based Transfer Learning). In the experiments, the balanced versions of these datasets (i.e. resDataset1, resDataset2) were taken into account as well as the original ones. The BERT-based Transfer Learning model achieved the highest classification success with 0.7864 macro-averaged F1 score values in resDataset1 and 0.7120 in resDataset2. It has been proven that the use of a pre-trained language model in Turkish datasets is more successful than other models in terms of classification performance.