Sentiment Analysis of Youtube Video Comments Using Artificial Intelligence: The Case of Trump-Zelensky Meeting

The diplomatic meeting between Donald Trump and Volodymyr Zelensky on February 28, 2025 had important consequences for international relations and was widely covered both in other countries and in the international press. Especially on social networking sites, many posts were made on the subject, and people commented on the shared content and expressed their feelings and thoughts. From this point of view, the aim of this study is to understand people’s emotional orientations and to determine their perceptions of the meeting by analyzing the emotions of the comments made on YouTube videos of the meeting. In the study, text mining method was used to analyze the comments. 226.000 comments on 4 videos shared on the YouTube platform were collected with the YouTube API interface using Python programming language. Then, the process of cleaning the obtained data was carried out. The cleaned data was divided into tokens and lemmatization was applied and the words were converted into root forms. A sentiment dictionary was then developed to prepare the data for analysis. VADER, Gensim, Pandas, Requests and NLTK libraries were used for data extraction, processing and analysis. Sentiment analysis technique was used to analyze the data obtained. In this context, the ChatGPT-4.5 artificial intelligence model of OpenAI was preferred and sentiment classification was performed with an unsupervised machine learning approach. The data were obtained from 4 videos of the Trump-Zelensky meeting on the YouTube accounts of CNN and Fox News television channels between February 28, 2025 and March 1, 2025. According to the results obtained from the research, anger, neutral and anticipation emotions were the most common emotions in the comments. However, negative emotional orientation predominates in the comments. The most frequently used words in the comments on the posts were Trump, Zelensky-Zelenskyy, America-American, Ukraine and War.