# Sentiment Analysis on Algerian Dialect with Transformers
## 4 Classification of Algerian YouTube Comments Using Transformers
### Model Inference
This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area. It also shows that there is a need for more research on sentiment analysis on Arabic dialects, specifically the Algerian dialect. This research opens the door for further studies on this topic and can be used as a foundation for future research in this field. In future work, there is substantial scope to enhance the model's robustness and generalizability by incorporating a more diverse and extensive dataset representing various Algerian dialects and exploring more advanced and diverse models to ascertain their efficacy relative to BERT in this specific context. Conceptualization, Z.B. ; methodology, Z.B., A.B. and A.F. ; software, A.F. and M.K. ; validation, A.B. and Z.B. ; investigation, A.F. and M.K. ; data curation, A.F. and M.K. ; writing--original draft preparation, A.F., M.K., Z.B. and A.B. ; writing--review and editing, Z.B. and A.B. ; visualization, Z.B. and A.F. ; supervision, A.B. and Z.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding

Not applicable. The data is available for download from: [[https://data.mendeley.com/datasets/zzwg3](https://data.mendeley.com/datasets/zzwg3) mnhsz/1]([https://data.mendeley.com/datasets/zzwg3](https://data.mendeley.com/datasets/zzwg3) mnhsz/1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task IDs** & **F1-Score** & **Accuracy** \\ \hline
1, 2, 3, 5, 7 & 0.7804 & 0.8100 \\
2, 3, 5 & 0.7803 & 0.8114 \\
**2, 3, 5, 11** & **0.7838** & **0.8174** \\
1, 2, 3, 5, 11 & 0.7816 & 0.8120 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation metrics when using the best combinations of preprocessing tasks. ## References

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