## 5 Conclusions  
In our work, we collected RUW related tweets and extracted societal emotions to showcase the neutral outlook of the public towards the RUW. The foremost reason for the neutrality could be the energy resource dependence of the majority of countries on the resource provider, Russia. Moreover, largely populated countries (such as India) preserving their neutral stand on the RUW can be the parallel cause of emotional neutrality. Furthermore, from the experiments performed, it was evident that our classification framework RUemo highlighted promising results, and the neural network-based MLP outperformed other traditional models.  
Although this research has explored a wide range of emotions for RUW context but it linguistically has limited scope. We have used English language-based tweets, whereas  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Multinominal NB & 89 & 90 & 89 & 89 \\ \hline Gaussian NB & 87 & 79 & 87 & 82 \\ \hline KNN Classifier & 93 & 91 & 93 & 92 \\ \hline RF & 86 & 77 & 86 & 80 \\ \hline DT & 92 & 94 & 92 & 93 \\ \hline LogReg & 95 & 94 & 95 & 95 \\ \hline SVC & 77 & 60 & 77 & 67 \\ \hline Adaboost & 80 & 67 & 80 & 73 \\ \hline MLP & 95 & 93 & 95 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics scores for all ML techniques.  
with the RUW impacting the entire globe, analyzing multilingual tweets is essential. This work performed the emotion analysis but further theme-based tweet analysis is needed. Another limitation of this work lies in the scope of method variability. Other emotion analysis techniques such as Text2 Emotion can also be used to extract the limited emotion classes that can be compared with this research's proposed framework. Thus, for future purposes, there is a need for a large dataset related to the RUW to gain more insights. More specifically, time-variant data are required to assess prewar, mid-war, and postwar societal outrey. However, this work highlighted the mid-war insights to gain societal attitudes during the war-like crises. Moreover, for emotion classification tasks, further deep-learning-based techniques need to be explored. Multilingual tweet emotional analysis is also required to gain more insights.  
This paper extends the scant RUW literature by revealing the feelings of the general public. This can further facilitate the development of suitable RUW-related norms and regulations by assisting government officials and war analysts in comprehending the general public's perception of the conflict. Practically, the suggested framework may be used to automate activities connected to text classification, such as the identification of online spam and false information. Any textual document-based data might be labeled using the suggested framework, which offers 27 different emotion classes for the classification process. In terms of methodology, this effort will make it possible for current researchers to use cutting-edge encoding and emotion analysis tools.  
This research received no external funding.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [53].  
**Acknowledgments:** The support from the Department of Computer Information Systems, College of Business Administration, Texas A&M University-Central Texas, is highly appreciated as it made this work publishable by providing resources.  
**Conflicts of Interest:** The authors declare no conflict of interest.