## 5 Experimental Results
It is obvious the difference in the resultant polarity classes when considering users' importance and influence on others. Due to the large percentage of low influencers in the considered dataset, we can notice the increase in the percentage of the neutral class for most texts due to the low effect of their users. In the OM process, we usually use any tool/technique to obtain a polarity score for a given text. Each tool may provide the same text a different polarity score due to many factors such as the tool's accuracy. In contrast, in our model, opinion holders can share the same opinion text, but due to our consideration of a user's weight, these same opinion texts should have different polarity scores based on their sharer's importance in the social network. To deal with such a case is similar to dealing with applying different OM techniques to the same text. In this case, the NL allows such a feature that it allows different observations for the same text and can obtain a polarity score for the problem text. Table 5 shows an example sample of retweeted texts through different influence level users. They were provided different scores and we reported how the NL dealt with such cases. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**User \#**} & \multirow{2}{*}{**User’s Weight (ANN)**} & \multirow{2}{*}{**User’s Influence Type**} & **Polarity Score** & **Polarity Score** \\  & & & **(TextBlob)** & **(Using Our Proposed NL Model (\(t\), \(i\), \(f\)))** \\ \hline
273 & 0.10 & Micro & 0.90 & (0.5, 0, 0) \\
518 & 0.44 & Macro & 0.90 & (0.91, 0, 0) \\
570 & 0.68 & Mega & 0.90 & (0.89, 0, 0) \\ \hline \multicolumn{4}{l}{Overall score for \((n=16)\) is \((0.79,0.02,0.03)\) with a final polarity score of 0.79 (SP)} \\ \hline
147 & 0.20 & Micro & 0.125 & (0, 0, 0) \\
212 & 0.31 & Macro & 0.125 & (0.16, 0, 0) \\
123 & 0.68 & Mega & 0.125 & (0.16, 0, 0) \\
283 & 0.75 & A-Lister & 0.125 & (0.50, 0, 0) \\ \hline \multicolumn{4}{l}{Overall score for \((n=20)\) is \((0.26,0,0)\) with a final polarity score of 0.26 (P)} \\ \hline
608 & 0.26 & Micro & 0.80 & (0.5, 0, 0) \\
473 & 0.44 & Macro & 0.80 & (0.89, 0, 0) \\
328 & 0.72 & Mega & 0.80 & (0.93, 0.83, 0.84) \\
285 & 0.92 & A-Lister & 0.80 & (0.93, 0, 0) \\ \hline \multicolumn{4}{l}{Overall score for \((n=40)\) is \((0.82,0.15,0.16)\) with a final polarity score of 0.82} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Samples of opinion dynamicity in the dataset. Figure 10: Polarity classes differences before and after considering user’s weight. For solving the problem of indeterminacy class, our proposed model highlighted the existence of undecided polarity and tried to identify it in the used dataset. Figure 11 shows the effect of such detection on the neutral polarity, where it is obvious that the neutral percentage in the dataset decreases from 69 to 51% due to the efficient identification of the undecided polarities in texts. Compared with cases #1 and #2, the neutral class normally increases while applying our proposed model due to the existence of low influencers whose tweets can be perceived as neutral. However, after considering the three components of NL, the neutral class increased (i.e., case #3) due to the existence of undecided polarities that were automatically considered neutral. By excluding these undecided cases, the final neutral class becomes an accurate representation of that class in the dataset. According to [8], small changes can shift feelings from being SP to P. Some opinions can still be SP ignoring any changes, while others may have different polarities at the same time. Undecided cases can happen and must be efficiently identified without approximation or dominant choice. Deep in the undecided cases identified in the dataset, Table 6 shows some undecided cases. Then Equation (10) was applied to solve such existence of undecided polarities. If undecided polarities are undecided after applying (10), then it was classified as indeterminate.