## 3 Proposed Work
### Emoticon Disambiguation
Then, after experimenting by combining the result of the EDLB and EDLU classifiers with different features--generated from the HARD dataset--using MLP-based models that formed hybrid models, some results have shown an improvement. Comparing the results of this work with other research showed that the suggested method improved the accuracy by 3.21% using the balanced HARD dataset and improved the accuracy by 2.17% using the unbalanced HARD dataset. In terms of future work, different avenues can be explored, including the following:

* Evaluate the use of context-based against context-free Arabic emoji and emoticon opinion lexicon using an Arabic dataset of emoji and emoticons. * Improve non-Arabic emoji opinion lexicon for automated opinion mining by adding emoticons to it through suggested methods and algorithms. Figure 6: Accuracy results of user opinion prediction for models in the literature for the models LR [17], RF, CNN, DT, GRU, BiLSTM, and MLP [20] compared with the proposed models EDLU-MLP and EDLB-MLP. * Work on more extensive extensible lists of emoji, emoticons, and negation words in both the standard Arabic language and dialectical Arabic. For instance, pictural symbols or negation words, combined with positive words, can be helpful to classify words while classifying text into negative when creating an Arabic corpus. * Test the proposed approach using a dataset from a different domain while applying a robustness method and statistical significance analysis of the results. * Apply an error detection and correction phase supporting the Arabic language as a preliminary step before applying the user opinion prediction approach to improve performance. Conceptualization, R.F.A.-M.; methodology, R.F.A.-M. and A.Y.A.-A. ; software, R.F.A.-M.; validation, R.F.A.-M. and A.Y.A.-A. ; formal analysis, R.F.A.-M. and A.Y.A.-A. ; investigation, R.F.A.-M. and A.Y.A.-A. ; resources, R.F.A.-M.; data curation, R.F.A.-M.; writing--original draft, R.F.A.-M.; writing--review and editing, R.F.A.-M. and A.Y.A.-A. ; supervision, A.Y.A.-A. ; project administration, R.F.A.-M.; All authors have read and agreed to the published version of the manuscript. This research received no external funding. The used HARD dataset and AraSenTi lexicon are publicly available from [17] and [33], respectively. The proposed ArEmo lexicons and the complete lists of Arabic booster and negation words are available at [[https://github.com/ralmutawa/files](https://github.com/ralmutawa/files)]([https://github.com/ralmutawa/files](https://github.com/ralmutawa/files)) (accessed on 27 April 2023). The authors declare no conflict of interest. ## Appendix A

This appendix includes the layer and output shape structure of the proposed ensemble-based deep learning model. ## References

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