## 3 Proposed Work
### Emoticon Disambiguation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**ArEmo Lexicon**} & \multicolumn{2}{c}{**Emoji Sentiment Ranking [18]**} & \multicolumn{2}{c}{**CF-Arab-ESL [38]**} & \multicolumn{2}{c}{**Arab-ESL [39]**} \\ \hline
**Emoji and Emoticon** & **Score** & **Emoji** & **Score** & **Emoji** & **Score** & **Emoji** & **Score** \\ \hline \hline  & 0.709 & \(\bigcirc\)3 & 0.221 & \(\bigcirc\)3 & 0.839 & \(\bigcirc\)3 & 0.272 \\ \(\bigcirc\)) & 0.417 & \(\bigcirc\)9 & 0.746 & \(\bigcirc\)9 & 0.946 & \(\bigcirc\)7 & \(-0.934\) \\ \(\bigtriangledown\) & \(-0.5\) & \(\bigcirc\)0.657 & \(\bigcirc\)9 & 0.911 & \(\bigcirc\)9 & 0.561 \\ \(\bigcirc\)9 & 0.829 & \(\bigcirc\)1 & 0.678 & \(\bigcirc\)2 & \(-0.4\) & \(\bigcirc\)4 & \(-0.678\) \\ \(\bigcirc\)( & \(-0.216\) & \(\bigcirc\)4 & \(-0.093\) & \(\bigcirc\)4 & \(-0.446\) & \(\bigcirc\)1 & 0.87 \\ \(\bigcirc\)9 & 0.692 & \(\bigcirc\)3 & 0.701 & \(\bigcirc\)9 & 0.946 & \(\bigcirc\)9 & 0.766 \\ \(\bigcirc\)9 & \(-0.118\) & \(\bigcirc\)2 & 0.644 & \(\bigcirc\)3 & 0.946 & \(\bigcirc\)5 & 0.227 \\ \(\bigtimes\)9 & \(-0.618\) & \(\bigcirc\)6 & 0.563 & \(\bigcirc\)2 & 0.786 & \(\bigcirc\)2 & 0.488 \\ \(\bigtimes\)9 & 0.594 & \(\bigcirc\)9 & 0.632 & \(\bigcirc\)2 & 0.839 & \(\bigcirc\)2 & 0.534 \\ \(\bigcirc\)9 & \(-0.406\) & \(\bigcirc\)8 & 0.52 & \(\bigcirc\)2 & 0.161 & \(\bigcirc\)9 & 0.616 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison table of the proposed ArEmo lexicon with related emoji lexicons in the literature. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \cline{2-6}  & & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** \\ \hline EDLB\({}_{\text{WE}}\) & 76.15\% & 75.90\% & 75.90\% & 76.15\% & 76.15\% & 75.74\% & 75.74\% \\ EDLB & 76.57\% & 76.38\% & 76.38\% & 76.57\% & 76.57\% & 76.22\% & 76.22\% \\ EDLU\({}_{\text{WE}}\) & 77.59\% & 76.81\% & 77.45\% & 72.05\% & 77.59\% & 74.02\% & 77.41\% \\ EDLU & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the proposed ensemble-based deep learning models EDLB and EDLU. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN-BiLSTM & 74.83\% & 74.75\% & 74.83\% & 74.47\% \\ CNN & 73.03\% & 72.72\% & 73.03\% & 72.55\% \\ BiLSTM & 75.06\% & 75.12\% & 75.06\% & 74.92\% \\ \hline EDLB & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of the ensemble model EDLB and its multiple internal models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN-BiLSTM & 74.83\% & 74.75\% & 74.83\% & 74.47\% \\ CNN & 73.03\% & 72.72\% & 73.03\% & 72.55\% \\ BiLSTM & 75.06\% & 75.12\% & 75.06\% & 74.92\% \\ \hline EDLB & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of the EDLU model per class. The experimental results for combining the proposed models with the produced features from the HARD dataset after applying the MLP machine learning algorithm are presented in Tables 12 and 13. The features subsequently are the number of positive words, the number of negative words, the total weight of positive words, the total weight of negative words, the number of negation words, the number of booster words, the number of repeated characters, the number of question marks, number of exclamation marks, the number of periods, the number of commas, the total weight of emojis and emoticons, and all features together. The tables show that only some generated features impact improving the performance. More robust insights about the effect of the generated features on enhancing the performance can be driven using an additional different dataset.