Çataltaş, MustafaÜstünel, BüşraBaykan, Nurdan Akhan2023-08-032023-08-0320232667-8055https://doi.org/10.36306/konjes.1173939https://search.trdizin.gov.tr/yayin/detay/1180910https://hdl.handle.net/20.500.13091/4489As Covid-19 pandemic affected everyone in various aspects, people have been expressing their opinions on these aspects mostly on social media platforms because of the pandemic. These opinions play a crucial role in understanding the sentiments towards the pandemic. In this study, Turkish tweets on Covid-19 topic were collected from March 2020 to January 2021 and labelled as positive, negative, or neutral in terms of sentiment using BERT which is a pre-trained text classifier model. Using this labelled dataset, a set of experiments were carried out with SVM, Naive Bayes, K-Nearest Neighbors, and CNN-LSTM model machine learning algorithms for binary and multi-class classification tasks. Results of these experiments have shown that CNN-LSTM model outperforms other machine learning algorithms which are used in this study in both binary classification and multi-class classification tasks.eninfo:eu-repo/semantics/openAccessArticle10.36306/konjes.1173939