## 5 Conclusions  
In our study, we evaluated the performances of several ML classifiers using two alternative vectorization approaches for the analysis of public sentiments about anti-government activities in English and Roman Urdu on Twitter data sets. The experimental results fluctuated with the change in the size of the data set, the number of features selected, and the language of the data set. In terms of accuracy, the performances of ML classifiers and vectorization approaches varied. Roman Urdu does not follow any standard form; our data set indicated a diverse range of our findings. We conclude that RF count vectorization works well with the small data set of Roman Urdu tweets as it tends to over-fit less when compared to other classifiers. It can also handle noisy data better and maintain accuracy due to bootstrapping. The MNB count vector outperformed other classifiers on the large data set as it makes the assumption that features are conditionally independent, given the class label, which makes it computationally more efficient. It can handle a large number of features effectively with low computational costs, which makes it a suitable choice for the large data set. The proposed study illustrates an analysis of Roman Urdu tweets, which is adequate for public sentiment analysis regarding forthcoming anti-government protests, demonstrations, assemblies, rallies, sit-ins, marches, etc. In addition, we observed that a large number of people in Pakistan tweet in pure Urdu. This study could be extended to pure Urdu tweets to improve the efficiency and effectiveness of sentiment analysis and gain valuable insight into public opinions.  
Conceptualization, T.M., S.A. ([PERSON]) and Q.H.A.; Methodology, T.M., N.H., S.A. ([PERSON]), M.S.H., S.A. ([PERSON]) and M.A.I.; Software, T.M., N.H. and S.A. ([PERSON]); Validation, N.H., A.T., S.A. ([PERSON]) and M.S.H.; Formal analysis, T.M. and A.T.; Investigation, T.M., A.T., M.S.H., F.T. and S.A. ([PERSON]); Data curation, N.H., A.T. and F.T.; Writing--original draft, T.M.; Writing--review & editing, N.H., F.T., S.A. ([PERSON]), Q.H.A. and M.A.I.; Visualization, A.T. and S.A. ([PERSON]); Supervision, S.A. ([PERSON]), Q.H.A. and M.A.I.; Project administration, S.A. ([PERSON]); Funding acquisition, M.A.I. All authors have read and agreed to the published version of the manuscript.  
This work is also supported in part by the Engineering and Physical Sciences Research Council (grant no. EP/X525716/1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F1 Score** \\ \hline RF & 0.69 & 0.66 & 0.66 \\ DT & 0.60 & 0.61 & 0.61 \\ SVM & 0.72 & 0.56 & 0.59 \\ MNB & 0.58 & 0.32 & 0.29 \\ GNB & 0.69 & 0.68 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Macro-average by using TFIDF vectorization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F1 Score** \\ \hline RF & 0.72 & 0.67 & 0.68 \\ DT & 0.61 & 0.62 & 0.61 \\ SVM & 0.67 & 0.57 & 0.58 \\ MNB & 0.75 & 0.74 & 0.73 \\ GNB & 0.71 & 0.72 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Macro-average by using count vectorization.  
**Data Availability Statement:** The data utilized for the analysis of research, which is stored on the servers of PSCA, is not accessible to the general public.  
**Conflicts of Interest:** The authors declare no conflict of interest.