## 3 Proposed Work
### Emoticon Disambiguation
The figure shows an improvement in the performance using both proposed EDLU-MLP and EDLB-MLP classifiers. UB indicates using unbalanced data with the model, while B indicates using balanced data. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Type of Data**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1-Score**} \\ \hline [PERSON] et al. [17] LR & Unbalanced & 76.1\% & - & - & - & - \\ [PERSON] et al. [20] RF & Balanced & 74.2\% & 74.0\% & 74.0\% & 74.0\% \\ [PERSON] et al. [20] CNN & Balanced & 67.4\% & 77.3\% & 67.4\% & 72.0\% \\ [PERSON] et al. [20] DT & Balanced & 66.4\% & 66.0\% & 66.0\% & 66.0\% \\ [PERSON] et al. [20] GRU & Balanced & 65.1\% & 75.4\% & 65.3\% & 69.8\% \\ [PERSON] et al. [20] BiLSTM & Balanced & 63.1\% & 72.5\% & 63.2\% & 67.4\% \\ [PERSON] et al. [20] MLP & Balanced & 22\% & 20\% & 21\% & 14\% \\ \hline EDLU-MLP & Unbalanced & 77.75\% & 76.42\% & 72.75\% & 74.36\% \\ EDLB-MLP & Balanced & 76.58\% & 76.39\% & 76.58\% & 76.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the experimental results for the proposed models using the balanced and unbalanced HARD datasets with models in the literature. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Applied Feature**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1-Score**} \\ \cline{3-8}  & & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** \\ \hline Num pos words & 77.74\% & 76.39\% & 77.63\% & 72.75\% & 77.74\% & 74.35\% & 77.62\% \\ Num neg words & 77.74\% & 76.40\% & 77.63\% & 72.75\% & 77.74\% & 74.36\% & 77.62\% \\ Weight pos words & 77.75\% & 76.42\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Num negation words & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Num booster words & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Num repeated characters & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Num question marks & 77.75\% & 76.40\% & 77.63\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Num exclamation marks & 77.74\% & 76.39\% & 77.63\% & 72.73\% & 77.74\% & 74.34\% & 77.61\% \\ Num periods & 77.74\% & 76.40\% & 77.63\% & 72.75\% & 77.74\% & 74.36\% & 77.62\% \\ Num commas & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ Emo score & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ All features & 76.33\% & 76.40\% & 77.61\% & 72.65\% & 77.72\% & 74.26\% & 77.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of the proposed EDLU-MLP model after experimenting with different features. ## 5 Conclusions and Future Work

It is challenging to classify user opinion, especially with higher than two opposite classifications that either are positive or negative. For a dataset consisting of five levels of ratings, such as HARD, this is due to the relative levels of rating in the same category of good or bad, confusion of reviewers to determine the exact rating, mistakes to assign a rating, and could also be intended as spam or a misleading one from a competitor for instance. In order to contribute to the Arabic literature that needs more research and resources of opinion mining, this work has suggested an ensemble deep learning-based approach to predict the five-star rates of user opinions after substituting each emoji or emoticon with their equivalent meaning. The developed EDLB and EDLU models, based on balanced and unbalanced HARD datasets, added the advantage of transfer learning through pre-trained word embeddings, particularly the fastText technique to convert the review's words into numerical representations.