## 3 Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline
**Models** & **0.01** & **0.02** & **0.03** & **0.04** & **0.05** & **0.06** & **0.07** & **0.08** & **0.09** & **0.1** \\ \hline M01 & 65.30 & 67.38 & 64.30 & 68.70 & 67.96 & 66.58 & 69.19 & 66.26 & 71.54 & 68.23 \\ \hline M02 & 64.40 & 68.31 & 66.29 & 68.61 & 72.39 & 69.25 & 71.44 & 69.13 & 73.74 & 70.36 \\ \hline M03 & 69.08 & 71.37 & 70.50 & 74.69 & 74.47 & 72.04 & 71.79 & 70.23 & 75.73 & 72.66 \\ \hline M04 & 71.33 & 72.34 & 73.96 & 78.57 & 78.44 & 75.61 & 77.93 & 72.48 & 79.87 & 77.84 \\ \hline M05 & 74.40 & 76.05 & 75.97 & 85.80 & 80.96 & 82.10 & 78.13 & 78.52 & 82.61 & 79.99 \\ \hline M06 & 77.47 & 76.36 & 81.48 & 86.36 & 85.51 & 86.28 & 81.70 & 82.45 & 87.68 & 84.26 \\ \hline M07 & 84.52 & 83.82 & 85.37 & 88.31 & 88.67 & 88.88 & 84.33 & 85.44 & 90.29 & 87.40 \\ \hline M08 & 89.37 & 83.48 & 90.36 & 92.05 & 92.42 & 92.57 & 88.82 & 90.22 & 95.89 & 90.33 \\ \hline M09 & 94.06 & 91.04 & 94.91 & 95.44 & 94.04 & 95.20 & 92.99 & 92.61 & 96.25 & 93.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average classification accuracy (%) results for an embedding size of 400. Figure 4: Average accuracy performance results for different embedding sizes: (**a**) embedding size of 200; (**b**) embedding size of 300; (**c**) embedding size of 400. Out of all the models under consideration, and particularly as compared models M01 and M02, when Word2 Vec is applied with CNN and BiLSTM, respectively, the response of the model is very poor. If BERT is used in place of Word2 Vec then some improvement can be observed in inaccuracy, which shows the effectiveness of the BERT model in text classification. The BERT model shows its supremacy over the Word2 Vec model, with improvements of 5% to 10% for sentiment classification. Models M05, M06, M07, and M08 also show improvements, but the proposed model shows the highest and most consistent performance for all datasets for the embedding size of 300 and dropout rate of 0.03. Since this combination showed consistent performance for other models, the embedding size 300 and dropout rate of 0.03 were implemented on all datasets for all models to conduct further experiments, as shown in Table 11.

### Evaluation of Multi-Fold Model of Sentiment Classification (MFSC)

To investigate the performance of a sentiment classification approach that relies solely on multi-dimensional sentiment modeling, the performance of the proposed multi-fold sentiment modeling method with XLNet (MFSC) shown in Table 12 and Figure 5 is compared with a CNN with Word2 Vec, BiLSTM with Word2 Vec, CNN with BERT, and BILSTM with BERT. The methods are discussed below. CNN with Word2 Vec: Firstly, Word2 Vec is used to initialize the vectorized word, following which CNN is applied to extract the features of the sentiments from the dataset, and finally a fully connected network is used for sentiment classification of the social media text. BiLSTM with Word2 Vec: In this instance, Word2 Vec is applied to achieve the word vectors, then BiLSTM is implemented for extraction of the sentiment characteristics of a given dataset, and finally a fully connected network is used for implement sentiment classification of the dataset. CNN with BERT: The initialization of the word vector is accomplished with the help of BERT, then the CNN is applied for extraction of the sentiment features of the dataset, and finally a fully connected network is used for sentiment classification of the dataset. BILSTM with BERT: Here, BERT is utilized to initialize the vector of words, followed by the BiLSTM technique being used for extraction of the sentiment features of the dataset, then in the last phase a fully connected network is used to implement sentiment classification of a dataset.