## 3 Proposed Work
### Emoticon Disambiguation
The experiments achieved the highest improved results using the weight of negative words for the unbalanced data and the total weight of emojis and emoticons for the balanced data. Table 14 compares the best-achieved results of balanced and unbalanced HARD datasets using the proposed models after emoji and emoticon replacement and using the features with the results of other studies in the literature that predicted user opinions based on five levels of ratings. For the balanced HARD data, using the EDLB-MLP model with the total weight of emojis and emoticons, the proposed classifier has achieved an increase of 3.21% in accuracy over [PERSON] et al. 's [20] top five models: random forest, convolutional neural network, decision tree, gated recurrent unit, and bi-directional recurrent neural network. The percentage increase formula is derived from the concept of percentage increase, as follows: [(New Accuracy \(-\) Old Accuracy)/Old Accuracy] \(\times\) 100. The authors in [20] have used an initial embedding layer to learn embedding jointly with their deep learning-based models added to the tokenization process without using additional features. In contrast, this study used the fastText framework to learn the word embeddings from the HARD dataset in conjunction with the ensemble learning technique. Employing this method with the MLP algorithm using the total weight of emojis and emoticons achieved a vast improvement of 237.27%, as compared to depending on the MLP algorithm without combining it with the proposed ensemble deep learning-based method. In the case of unbalanced HARD data, the proposed EDLU-MLP model, with the weight of negative \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \cline{3-8}  & & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** \\ \hline CNN-BiLSTM & 76.79\% & 75.98\% & 76.79\% & 71.25\% & 76.79\% & 73.24\% & 76.69\% \\ CNN & 75.78\% & 75.06\% & 75.86\% & 70.11\% & 75.78\% & 72.04\% & 75.66\% \\ BiLSTM & 77.26\% & 75.12\% & 77.13\% & 72.97\% & 77.26\% & 73.91\% & 77.13\% \\ \hline EDLU & 77.75\% & 76.40\% & 77.64\% & 72.75\% & 77.75\% & 74.36\% & 77.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of the ensemble model EDLU and its multiple internal models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Applied Feature**} & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Num pos words & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num neg words & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Weight pos words & 76.56\% & 76.37\% & 76.56\% & 76.21\% \\ Weight neg words & 76.56\% & 76.37\% & 76.56\% & 76.21\% \\ Num negation words & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num booster words & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num repeated characters & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num question marks & 76.56\% & 76.37\% & 76.56\% & 76.21\% \\ Num exclamation marks & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num periods & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Num commas & 76.57\% & 76.38\% & 76.57\% & 76.22\% \\ Emo score & 76.58\% & 76.39\% & 76.58\% & 76.23\% \\ All features & 76.54\% & 76.36\% & 76.54\% & 76.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of the proposed EDLB-MLP model after experimenting with different features. words, had an increase of 2.17% in accuracy as compared with [PERSON] et al. [17], where the authors used a logistic regression model using unigram and bigram features with TF-IDF. Similar to [PERSON] et al. [17], the accuracy result of the proposed model, using an unbalanced HARD dataset, is higher than the proposed model using a balanced HARD dataset. As accuracy is the common metric between the suggested models and the models in the literature, Figure 6 represents the performance comparison between all models using accuracy.