## 7 Conclusions and Future Works  
Due to the continuous and increasing use of social networks and E-commerce sites, many platforms depend on the analysis of user opinions to improve the provided services and measure customer satisfaction. One of sentiment analysis's most common problems is the language customers use to express their opinions. The Arabic language is considered one of the most difficult languages in the world because it contains complex linguistic terms and many different dialects that may be used in the same comment or review, making the analysis process more difficult. This paper provides a clear view of recent sentiment analysis approaches and algorithms that depend mainly on ML and lexicon approaches for storing and analyzing a dataset. A comparison of recent research strategies is also provided to compare different methodologies with applied language, including the Arabic language, and their achieved performance. An advanced methodology is proposed using cross-validation that divides the sentiment analysis documents and terms into \(k\)-folds for training and testing. Data vectorization is applied using the TF-IDF algorithm for counting polarity terms, and an encoder is generated from the training dataset to be applied to the testing dataset. Furthermore, the paper provided an algorithm called FFF-SA based on a forward filter of feature selection that measures and scores the accuracy for each \(k\)-chuck feature and the following accumulative features. The scoring is processed by executing three feature importance methods, Pearson, Chi\({}^{2}\), and RF, with eight ML models where each feature importance is executed with each ML model. Each experiment measures and scores the recorded accuracy for each \(k\)-chunk feature and then adds accumulative feature to measure the accuracy again. The results proved that the best accuracy is recorded with RF feature importance with the NB model. Future research directions will be directed to apply the same methodology and algorithm on additional Arabic datasets and apply deep learning models to measure the performance and accuracy.  
Data curation, A.M.M.; formal analysis, A.M.M. and M.E.; investigation, A.M.M., M.A. ([PERSON]) and A.A.; supervision, M.A. ([PERSON]) and A.A.; writing--original draft, M.E. and M.A. ([PERSON]); writing--review and editing, A.M.M., M.E., M.A. ([PERSON]), and A.A. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**No. of Classes**} & \multirow{2}{*}{**Dialect**} & \multirow{2}{*}{**Language**} & \multicolumn{2}{l}{**Annotation**} & \multirow{2}{*}{**Performance**} \\ \cline{3-4} \cline{6-6}  & & & & & **Algorithm** & & \\ \hline \multirow{2}{*}{[17]} & \multirow{2}{*}{2020} & Pos.–Neg. & Single & Arabic & Lexicon-based & 77\% \\ \cline{3-4} \cline{6-6}  & & Pos.–Neg. & Single & Arabic & Lexicon-based & 86\% \\ \hline \multirow{2}{*}{[19]} & \multirow{2}{*}{2022} & Pos.–Neg. & Single & Arabic & Lexicon-based & 76.48\% \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{2023} & Pos.–Neg. & Single & Arabic & Lexicon-based & Senti. Score \\ \hline \multirow{2}{*}{[23]} & \multirow{2}{*}{2016} & Pos.–Neg. & Single & Arabic & Lexicon-based & 84\% \\ \hline \multirow{2}{*}{[40]} & \multirow{2}{*}{2021} & Pos.–Neg. & Single & Arabic & ML-based & 76.9\% \\ \hline \multicolumn{4}{l}{Proposed FFF-SA} & \multirow{2}{*}{Pos.–Neg.} & Single & Arabic & ML-based & 84.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with recent sentiment analysis performance.  
Figure 7: Confusion Matrix for the final winning model.  
This research received no external funding.