## 3 Proposed Methodology
### Algorithm Flow of TextCNN + RoBERTa and RNN_Bert_Based
The results clearly demonstrate that the RNN_Bert_based model exhibits a substantial degree of superiority over the other methods that were subjected to evaluation in this study. Figure 12a provides a detailed comparison of the recall achieved by the RNN_Bert_based model against other methods, demonstrating its superior performance. In particular, the RNN_Bert_based model exhibits a recall rate of 94.80%, which is markedly higher than that of the alternative models. Similarly, Figure 12b presents a comparison of the F1 score obtained by the RNN_Bert_based model with those of other methods. The results clearly demonstrate that the RNN_Bert_based model not only matches but also exceeds the performance of the other models, achieving an impressive F1 score of 94.80%. This consistent performance across multiple metrics serves to underscore the effectiveness of the RNN_Bert_based model in the context of text classification tasks, thereby rendering it a more robust and reliable approach in comparison to existing methods. Figure 10: RNN_Bert_based accuracy comparison with other methods. ## 5 Conclusions

In conclusion, this paper presents a text classification model based on the RNN_Bert_based architecture. The results of experimentation clearly demonstrate the superiority of the proposed RNN_Bert_based model in sentiment analysis and classification tasks. With an accuracy of 94.8%, it outperforms other models such as KNN_Bert_based, TextCNN + RoBERTa, ALBERT, and XLNet in key metrics including precision, recall, and F1 score. This indicates the effectiveness of integrating RNN architectures with Bert-based embeddings, offering substantial improvements in accuracy and generalization. The findings underscore the model's potential to significantly enhance NLP and machine learning research, providing a robust solution for various text classification challenges. The performance evaluation underscores the advancements made in NLP through the development of the proposed RNN_Bert_based model to overcome the existing work of TextCNN + RoBERTa [14], ALBERT, XLNet, Bert, and a newly developed model of KNN_Bert_based model. Each of the two models can provide significant improvements over traditional approaches and demonstrate their effectiveness in various NLP tasks. In the future, we also aim to explore enhancing Bert by integrating advanced AI techniques, such as reinforcement learning or attention-based mechanisms, for more efficient text classification, so that we can obtain an approach that could lead to more accurate, faster, and scalable text classification,

Figure 11: A comparison of the RNN_Bert_based model’s precision with other methods. Figure 12: A comparison of RNN_Bert_based model’s recall with other methods (**a**), A comparison of RNN_Bert_based model’s F1 score with other methods (**b**). particularly in specialized domains like medical diagnostics, legal document analysis, or sentiment detection in social media. Data collection, C.E. ; conceptualization, C.E. ; methodology, C.E. ; experimentation, C.E. ; writing--original draft preparation, C.E. ; software development and analysis, C.E. ; providing research direction and checking on papers, S.L. ; review, S.L. ; editing, S.L. ; investigation, S.L. ; validation, S.L. ; project administration, S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. ## References

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