Acta Infologica acin 2024, 8 (1), 23–33 DOI: 10.26650/acin.1418834 Research Article / Araştırma Makalesi Sentiment Analysis on GPT-4 with Comparative Models Using Twitter Data* Mustafa Özel 1 , Özlem Çetinkaya Bozkurt 2 1 Burdur Mehmet Akif Ersoy University, Social Sciences Institute, Burdur, Türkiye 2 Burdur Mehmet Akif Ersoy University, Bucak Faculty of Business Administration, Department of Business Administration, Burdur, Türkiye Corresponding author : Mustafa Özel E-mail : mozel@mehmetakif.edu.tr *Bu çalışma 10. Uluslararası Yönetim Bilişim Konfer- ansında (IMISC 2023) sözlü olarak sunulan bildirinin geliştirilmiş halidir. Submitted : 12.01.2024 Revision Requested : 20.03.2024 Last Revision Received : 15.04.2024 Accepted : 16.04.2024 Published Online : 03.06.2024 This article is licensed un- der a Creative Commons Attribution- NonCommercial 4.0 International Li- cense (CC BY-NC 4.0) ABSTRACT Every day, people from all over the world use Twitter to talk about many different topics using hashtags. Since ChatGPT was launched, researchers have been study- ing how people perceive it in society. This research aims to find out what Turkish Twitter users think about OpenAI’s latest AI model called Generative Pre-trained Transformer 4 (GPT-4). The quantitative data used in this study consist of hashtags on the topic of GPT-4 and involve 2,978 tweets on this topic that were shared on Twitter between March 14-April 9, 2023. The study uses TextBlob sentiment scores to classify the tweets and support vector machines, logistic regression, XGBoost, and random forest algorithms to classify the sentiment of the dataset. The results from the logistic regression, XGBoost, and support vector methods are in close alignment. All parameter findings indicate dependable machine learning, emphasizing the models’ success in classifying tweet sentiment. Keywords: Sentiment analysis, social media, Twitter, X, natural language processing 23