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The beginning of this decade brought utter international chaos with the COVID-19 pandemic and the Russia-Ukraine war (RUW). The ongoing war has been building pressure across the globe. People have been showcasing their opinions through different communication media, of which social media is the prime source. Consequently, it is important to analyze people’s emotions toward the RUW. This paper therefore aims to provide the framework for automatically classifying the distinct societal emotions on Twitter, utilizing the amalgamation of Emotion Robustly Optimized Bidirectional Encoder Representations from the Transformers Pre-training Approach (Emoroberta) and machine-learning (ML) techniques. This combination shows the originality of our proposed framework, i.e., Russia-Ukraine War emotions (RUemo), in the context of the RUW. We have utilized the Twitter dataset related to the RUW available on Kaggle.com. The RUemo framework can extract the 27 distinct emotions of Twitter users that are further classified by ML techniques. We have achieved 95% of testing accuracy for multilayer perceptron and logistic regression ML techniques for the multiclass emotion classification task. Our key finding indicates that:First, 81% of Twitter users in the survey show a neutral position toward RUW; second, there is evidence of social bots posting RUW-related tweets; third, other than Russia and Ukraine, users mentioned countries such as Slovakia and the USA; and fourth, the Twitter accounts of the Ukraine President and the US President are also mentioned by Twitter users. Overall, the majority of tweets describe the RUW in key terms related more to Ukraine than to Russia.
Vyas et al. (Fri,) studied this question.