## 4 Results and Discussion  
### Analysis of Experimental Findings  
We describe and evaluate the outcomes of our experiments in this section. Experiments using lexical- and multifactor-sentiment analyses were conducted to assess the performance of the suggested algorithm, which included the consideration of emoticons, intensifiers, and negations. We adopted usual text classification parameters of precision (P), recall (R), accuracy (Acc), and F measure (F1) to examine the alternative techniques. F measure, a harmonic mean of retention and accuracy, is used to assess overall system effectiveness [45].  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}} \tag{3}\]  
\[\text{F1}=\frac{2\text{PR}}{\text{P}+\text{R}} \tag{4}\]  
Categorization performance and the average F-SC among NEG and POS categories are shown in Table 15 and Figure 2, respectively. The experiments were run using a gold-labeled copy of our original records. We tested a variety of parameters that could reflect sentiment polarity or strength, as previously noted. Table 15 shows that the strategy incorporating all aspects (LX-based baseline + light stemming + polarity + negations + emoji + intensification words) achieved the highest classification outcomes, with precision SC of 89.80% and F-SC of 86.32%, improved performance of 5% and 9%, respectively, over the base point. Table 15 further demonstrates that the LX-based baseline had an excellent classification precision of 84.34% and F-SC of 76.47% because of, first, the knowledge-based strategy that enabled collecting particular field characteristics, and second, the efficacious development of Saudi dialect LXs (see Table 16).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline TT & _Vision like \(\downarrow\Emoji, on the other hand, had a lesser rate of precision than that of the baseline method, with an F-SC of 48.70% and 82.63% accuracy rate. The usage of emoji to convey a sarcastic message may have influenced the quality of the evaluation in some situations. Integrating the LX- and polarity-based classifications achieved classification accuracy of 88.94% and an F-SC of 82.14%, as shown in Table 15. Merging LX-based and special phrases resulted in an accuracy rate of 85.39% and an F-SC of 76.99%. In considerations of light stemming, classification precision SC and F-SC were 88.99% and 81.16%, respectively, when combined with the LX-based technique.  
In order to assess most negation phrases and avert misstatements owing to their inconsistent implementation, negation is a more complicated task that requires particular guidelines. The complexity of the Arabic language and the question of negation necessitated indepth linguistic analysis and semantic synthesis. The LX-based method's negative  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Corpus** & **Domain** & **Accuracy** & **F-SC** & **Precision** & **Recall** \\ \hline Aldayel [46] & 1103 TTs & Multidomain (social issues) & 78.22\% & 77.64\% & 75.49 & 77.02 \\ Al-Twairesh [22] & 4700 TFs & Multidomain & 78.61\% & 62.94\% & 57.30 & 63.17 \\ MULDASA & 7000 TFs & Specific domain & & & \\  & (unemployment) & 89.80\% & 84.70\% & 86\% & 86.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Findings from Al-Twairesh and Adayel corpora using our LX-based technique.  
Figure 2: Findings of multifactor LX-based sentiment analysis of social-media posts in dialectical Arabic.  
Our LX-based strategy outperformed the other two, with a 10% precision enhancement over the Adayel corpus and a 2% enhancement in the F-SC over the Al-Twairesh technique. Many aspects contribute to lexical analysis, comprising intensifiers, negations, proverbs, and interjections, and a full multi-intensity LX for the Saudi dialect. The favorable outcomes of our technique could have been due to these features. Our technique can be applied to other fields, as shown by the comprehensive review.