In this new age of social media, understanding the public sentiment is a crucial endeavor with complex implications. This work presents a Twitter sentiment analysis model covering tweets related to ChatGPT and recently launched GPT-4, a revolutionary language model. Hence, these tweets open up new realm opportunities for AI development. Our model performs competently with a combination of Natural Language Processing techniques and machine learning models. Extensive tests are performed on three machine learning models–Logistic Regression, Naive Bayes, and Support Vector Machine–using parameters as accuracy, precision, recall, F1-Score, and support. Notably, with an accuracy of 83.41% and a balanced F1-Score of 0.7666, our results suggest Logistic Regression as the optimal model, demonstrating its efficiency in classifying sentiments of ChatGPT and GPT-4 tweets. This study is a pioneering step in the field of sentiment analysis, exploring the largely uncharted areas by analyzing the sentiment of GPT-4 tweets, thus providing vital insights into understanding, and utilizing social media sentiment within this specific context and encouraging further progress.