## 4 Proposed Sentiment Analysis Methodology  
As discussed in the previous sections, recent sentiment analysis mechanisms depend mainly on different aspects. Firstly, the applied language or dialect contains users' reviews and comments. Secondly, the data preprocessing mechanism for cleaning and adapting \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Dataset Size** & **No. of Classes** & **Dialect** & **Language** & **No. of Annotators** & **Performance** \\ \hline
[17] & 30K & Pos.–Neg. & Single & Arabic & 2 & 77\% \\ \cline{2-7}  & 8K & Pos.–Neg. & Single & Arabic & 2 & 86\% \\ \hline
[19] & 16.9K & Pos.–Neg. & Single & Arabic & 2 & 76.89\% \\ \cline{2-7}  & 18.9K & Pos.–Neg. & Single & Arabic & 2 & 76.48\% \\ \hline
[21] & 5k & Pos.–Neg. & Single & Arabic & 2 & Senti. Score \\ \cline{2-7}  & Pos.–Neut.–Neg. & & & 3 & Senti. Score \\ \hline
[22] & 100K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 66\% \\ \hline
[23] & 1.1K & Pos.–Neg. & Single & Arabic & 2 & 84\% \\ \hline
[24] & 3.4K & Pos.–Neut.–Neg. & Single & Arabic & 3 & Senti. Score \\ \hline
[25] & 6.3K &
\begin{tabular}{c} High Pos.–Pos. \\ Neut. \\ Neg.–High Neg. \\ \end{tabular} & Single & Arabic & 5 & Senti. Score \\ \hline
[26] & 6K & Pos.–Neut.–Neg. & Single & English & 3 & Senti. Score \\ \hline
[27] & 3.9K & Pos.–Neut.–Neg. & Single & English & 3 & Senti. Score \\ \hline
[28] & 12.5K &
\begin{tabular}{c} High Pos.–Pos. \\ Neut. \\ Neg.–High Neg. \\ \end{tabular} & Single & English & 5 & 46.4\% \\ \hline
[29] & 22.5K & Pos.–Neut.–Neg. & Single & English & 3 & 86.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of lexicon-based methods with their performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Dataset Size** & **No. of Classes** & **Dialect** & **Language** & **No. of Annotators** & **Performance** \\ \hline
[1] & 20K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 79\% \\ \hline
[5] & 21K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 75.1\% \\ \hline
[31] & 24K & Pos.–Neut.–Neg. & Single & Turkish & 3 & 91.57\% \\ \hline
[32] & 80.8K & Pos.–Neut.–Neg. & Multi &
\begin{tabular}{c} Russian \\ Kazakh \\ \end{tabular} & 3 & 77\% \\ \hline
[33] & 39.8K & Pos.–Neut.–Neg. & Single & English & 3 & 89.92\% \\ \hline
[34] & 10K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 83\% \\ \hline
[36] & 685 & Pos.–Neg. & Multi &
\begin{tabular}{c} Eng. Spa. Ita. \\ Ger. Fre. \\ \end{tabular} & 2 & 88.17\% \\ \hline
[37] & 6.7K & Pos.–Neut.–Neg. & Single & English & 3 & 78.76\% \\ \hline
[38] & 8K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 78.46\% \\ \hline
[39] & 4.5K & Pos.–Neut.–Neg. & Single & Arabic & 3 & 82.39\% \\ \hline
[40] & 14.6K & Pos.–Neg. & Single & English & 2 & 76.9\% \\ \hline
[41] & 11K & Pos.–Neg. & Multi & \begin{tabular}{c} Urdu \\ Roman Urdu \\ English \\ \end{tabular} & 2 & 84\% \\ \cline{2-7}  & 11K & Pos.–Neg. & Multi & \
\begin{tabular}{c} Roman Urdu \\ English \\ \end{tabular} & 2 & 85\% \\ \cline{2-7}  & 11K &
\begin{tabular}{c} English \\ English \\ \end{tabular} & 77\% \\ \hline
[43] & 0.5K & Pos.–Neut.–Neg. & Single & English & 3 & 89.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ML-based models with their performance. datasets and corpus documents for sentiment analysis. Thirdly, the implemented lexicon-based or ML-based methods for manipulating datasets. Finally, the proposed methodology and algorithm for exploring and enhancing the analysis of user polarity is based on word terms and polarity terms. The methodology of this paper is based on applying ML-based sentiment analysis algorithms on Arabic datasets from [1; 51]. The Arabic language is a difficult language in the ramifications of its terminology as it contains many dialects and huge linguistic and morphological terms, which makes analyzing user sentiments a great challenge. The Arabic language has low resources and contains high morphological and linguistic terms [47]. Therefore, the analysis and classification of polarity terms is considered a challenging process.