## 3 Proposed Model
Initially, the vectorized word is populated with the help of Word2 Vec, and then with a CNN-based attention mechanism, the emotional characteristics of the dataset are retrieved from different levels of words, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Dataset 1** & **Dataset 2** & **Dataset 3** & **Average** \\ \hline
**M01** & 72.36 & 71.53 & 71.98 & 71.96 \\ \hline
**M02** & 73.65 & 74.01 & 73.56 & 73.74 \\ \hline
**M03** & 75.98 & 76.62 & 77.02 & 76.54 \\ \hline
**M04** & 77.62 & 78.65 & 77.12 & 77.80 \\ \hline
**M05** & 83.65 & 84.21 & 84.32 & 84.06 \\ \hline
**M06** & 86.33 & 85.98 & 86.1 & 86.14 \\ \hline
**M07** & 89.32 & 88.75 & 89.1 & 89.06 \\ \hline
**M08** & 92.62 & 92.78 & 91.92 & 92.44 \\ \hline
**M09** & **95.51** & **95.32** & **95.98** & **95.60** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Sentiment classification accuracy results using the MLSC. Figure 6: Graphical representation of the performance results with the MLSC. sentences, and phrases. Lastly, the completely linked network is used to implement the sentiment classification. MLSC with BILSTM and Word2 Vec: This is a Word2 Vec, BiLSTM, and MDSC-based sentiment categorization algorithm. Here, Word2 Vec is used to initialize the word vector, and then BiLSTM is used to extract sentiment features of the dataset from different levels of words and sentences using an attention mechanism. Finally, the completely linked network is used for sentiment classification in the given dataset. MLSC with CNN and BERT: This is a BERT, CNN, and MDSC-based sentiment classification approach. The word vector's initialization is achieved using BERT, and then the CNN is utilized to extract the sentiment features of the dataset from different levels, as discussed using an attention mechanism. Finally, the completely linked network is used for the sentiment classification of the dataset. MLSC with BILSTM and BERT: This is a BERT, BiLSTM, and MDSC-based sentiment classification approach. BERT is used to initialize the word vector, and then BiLSTM is utilized to extract the sentiment features of the dataset from the given levels using an attention mechanism. In the final phase, using a fully interconnected computer network, the dataset classification process is carried out

### Assessment of Multi-Fold and Multi-Level Modeling of Sentiment Method (MFMLSC)

To assess our method's overall performance, the performance results in terms of the multi-fold and multi-level classification for the sentiment method are compared with the methods discussed in the previous section



## 5 Conclusions

We observed that the autoregressive-based model for sentiment classification that uses the pre-trained word vector XLNet showed the greatest classification accuracy, with an average of 97.30% accuracy for all datasets. The proposed model solved the problem of the lack of semantic information in reviews, which affects the accuracy during classification. The experimental findings demonstrated that when compared to the current methods, our method significantly increases the accuracy of the sentiment classification process for social media datasets. **Author Contributions:** Methodology, R.R., D.P., A.K.R. and P.S. ; Software, R.R., A.K.R. and P.S. ; Validation, D.P. and A.K.R. ; Formal analysis, P.S., A.V. and D.G. ; Investigation, A.V. and D.G. ; Resources, P.R.K. ; Data curation, P.R.K. ; Writing--original draft, P.R.K. ; Writing--review & editing, S.N.M. ; Visualization, S.N.M. ; Supervision, S.N.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** Article processing charges supported by School of Computer Science & Engineering(SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India. **Conflicts of Interest:** The authors declare no competing interests.