International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 17, No. 5, 8 Oct. 2025
Cover page and Table of Contents: PDF (size: 2187KB)
Sentiment Analysing and Visualising Public Opinion on Political Figures across YouTube and Twitter Using NLP and Machine Learning
PDF (2187KB), PP.117-164
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Author(s)
Victoria Vysotska
1
Alina Starchenko
1
Lyubomyr Chyrun
2
Zhengbing Hu
3
Yuriy Ushenko
4,*
Dmytro Uhryn
4
Index Terms
Sentiment Analysis, Public Opinion, Social Networks, Twitter, Youtube, Ukrainian-Language Content, Natural Language Processing, NLP, Machine Learning
Abstract
The study is devoted to the analysis of public sentiment towards Ukrainian political figures based on comments on social media, in particular, YouTube and Twitter. The work aims to identify differences in the perception of political leaders and to understand how the platform affects the tone of statements. The main research question is to determine how public opinion about politicians in Ukraine differs between YouTube and Twitter during the full-scale war. To do this, a corpus of comments and tweets from 2022 to 2023 was collected, which went through pre-processing stages (including cleaning up slang and spelling mistakes). The article presents the results of a comprehensive analysis of public opinion on five public figures of Ukraine (S. Prytula, P. Poroshenko, V. Zelensky, S. Sternenko, A. Yermak) based on data from the social networks YouTube and Twitter. For data collection, the YouTube Data API and the Apify platform were used, a corpus of Ukrainian-language comments and tweets was collected and processed, which went through the stages of purification, normalisation and lemmatisation, taking into account slang, surzhyk and spelling mistakes. The sentiment analysis model, built on the basis of multilingual-e5-base embeddings and the XGBClassifier algorithm, showed an accuracy of 89.4%, macro-F1 of 88.7%, and a weighted F1 of 89.1%. Sentiment distribution analysis revealed that, on average, 42% of messages were positive, 36% were negative, and 22% were neutral. Twitter had a higher share of negative statements (up to 40%), while YouTube had a predominance of positive sentiment (up to 47%). The results indicate differences in the perception of public figures on different platforms and confirm the effectiveness of the developed approach for the Ukrainian-speaking segment of social networks. The results indicate significant differences in sentiment distribution: comments on YouTube are more likely to be marked by emotional intensity and harshness. At the same time, Twitter exhibits a more concise but no less polarised discourse. One of the reasons for this difference may be the difference in the format of the platforms, their audience, and the speed of content distribution. Further research should take into account the impact of user demographic biases, as well as the activity of bots or coordinated campaigns that can change the perception of public opinion. The practical significance of the study lies in the fact that its results can be used by politicians, journalists, and public figures to better understand the mood of society, predict reactions to political events, and build more effective communication. At the same time, it is worth noting that there are limitations: automated sentiment analysis has difficulty detecting sarcasm, irony, or context-sensitive meanings, which can affect the Accuracy of the results. In addition, the study takes into account the ethical aspects of data collection and analysis: only publicly available comments were used, without interference in the private sphere of users. There are possible risks of abuse of such technologies, and the need for responsible application of the findings is emphasised.
Cite This Paper
Victoria Vysotska, Alina Starchenko, Lyubomyr Chyrun, Zhengbing Hu, Yuriy Ushenko, Dmytro Uhryn, "Sentiment Analysing and Visualising Public Opinion on Political Figures across YouTube and Twitter Using NLP and Machine Learning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.5, pp. 117-164, 2025. DOI:10.5815/ijigsp.2025.05.08
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