## 5 Experimental Results  
To effectively validate the proposed model, a desktop program was developed using Python, for implementing TextBlob, and the matrix laboratory (MATLAB) libraries for implementing ANN and NL. Toolboxes for ANN and fuzzy logic were used to build the proposed ANN and NL classifiers. The fuzzy toolbox was used due to the unavailability of a toolbox for NL until this time and the proven possibility of implementing NL using the fuzzy toolbox as mentioned in [9]. The dataset already collected and filtered in [10] was used as a consequence of the absence of a benchmark dataset that combines the opinion \begin{table}
\begin{tabular}{c c} \hline \hline
**Rules \#** & **Rules** \\ \hline  
-t’, THEN PS is ’NEG-t’ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample of the designed NL rules. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Score** & **Polarity** \\ \hline \(n\ >\ 0.75\) & Strong Positive \\ \(0.25\ <\ n\ \ \leq\ 0.75\) & Positive \\ \(0\ <\ n\ \ \leq\ 0.25\) & Weak Positive \\ \(0\)/**NULL** & Neutral/undecided \\ \(-0.25\ \leq\ n\ <\ 0\) & Weak Negative \\ \(-0.75\ \leq\ n\ <-0.25\) & Negative \\ \(n\ <\ -0.75\) & Strong Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The seven polarity levels [10]. text, its writer's following/followers list, and its tweet reaction counts. Table 3 documents the polarity classification of 1080 collected tweets using TextBlob. Regarding the contribution phase of adding a new element into the basic opinion elements, the constructed ANN operated with efficient performance and attempted to classify the opinion holders of the dataset into the four mentioned types. Table 4 reports the resultant ANN classification errors of each of the opinion holder types. It was obvious that the constructed ANN trained more on the low-level influencers than on the high-level influencers. This can be due to the abundant presence of the low influencers in training and real-life compared with the presence of the high influencers. Figure 9 emphasizes this observation and shows the results of the ANN classification of the opinion holders in the used dataset. The lower influencers have the highest percent, and this can be a caution for the decisionmakers to not rely on the traditional OM results while dealing with OSNs without classifying their opinion holders, as there would be spammers and people of low impact whose opinions are not that worthwhile. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Overall Items**} & \multicolumn{4}{c}{**Average Error in Opinion Holders ANN Classification**} \\ \cline{2-5}  & **Low Influence** & **Moderate** & **High Influence** \\ \hline \multirow{4}{*}{1080} & 0.099 & 0.239 & 0.251 \\ \cline{2-5}  & Micro & Macro & Mega & A-Listers \\ \cline{2-5}  & 0.089 & 0.234 & 0.248 & 0.208 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average errors in the ANN classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Overall** \\ **Items** \\ \end{tabular} } & \multicolumn{4}{c}{**Polarity Classes and Distributions**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Positive**} & \multicolumn{2}{c}{**Neutral**} & \multicolumn{2}{c}{**Negative**} \\ \hline \multirow{4}{*}{1080} & \multicolumn{4}{c}{559 (51.76\%)} & \multicolumn{4}{c}{65 (6.02\%)} \\ \cline{2-7}  & SP & P & WP & \multirow{2}{*}{456 (42.22\%)} & SN & NEG & WN \\ \cline{2-7}  & 150 & 232 & 177 & & 1 & 32 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Polarity distributions of the dataset using TextBlob. Figure 9: Opinion holders’ classification in the dataset. According to our proposed model, opinion holders on OSNs share opinions of the same polarity as others, but they should vary in their obtained OM polarity scores due to their influence on the audience. Figure 10 shows the polarity change from a regular OM process with its basic opinion elements using TextBlob and after integrating the new element of opinion (i.e., user's weight) into them using NL.