## 3 Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**M01** & _CNN_ & Word2 Vec & ReLu & 300 & 0.03 & _sgd_ & 50 & 512 \\ \hline
**M02** & _BiLSTM_ & Word2 Vec & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M03** & _CNN_ & BERT & ReLu & 300 & 0.03 & _sgd_ & 50 & 512 \\ \hline
**M04** & _BILSTM_ & BERT & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M05** & MFMLSC & Word2 Vec & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M06** & MFMLSC & Word2 Vec & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M07** & MFMLSC & BERT & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M08** & MFMLSC & BERT & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline
**M09** & MFMLSC & XLNet & ReLu & 300 & 0.03 & _sgd_ & 50 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Hyperparameters settings. MFSM with CNN and Word2 Vec: The Word2 Vec, CNN, and MFSM approaches are used to classify sentiments. To begin, emoj-based symbols are treated as language symbols in a social media text. Next, Word2 Vec is implemented to for the initialization of the word vector, and the CNN extracts sentiment characteristics from the dataset. Finally, the sentiment categorization approach is accomplished through a completely connected network. MFSM with CNN and BERT: the BERT, CNN, and MFSM approaches are used to create a sentiment classification system. To begin, both language symbols and emoticon symbols are handled in datasets in the same manner as language symbols. Next, BERT is used for the initialization of the word vector, and the CNN is implemented to extract the emotional components of the dataset. Finally, the sentiment categorization approach is accomplished through a completely connected network. MFSM with BiLSTM and Word2 Vec: The Word2 Vec, BiLSTM, and MFSM-based sentiment categorization approaches are used. To begin, all symbols in a dataset, including language symbols and emoticon symbols, are regarded as language symbols. The vector of the word is then initialized using Word2 Vec, and the BiLSTM model extracts features of sentiments from the dataset. Finally, the sentiment categorization approach is accomplished through a completely connected network. \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 & 82.98 & 83.12 & 83.25 \\ \hline
**M06** & 85.61 & 84.52 & 86.32 & 85.48 \\ \hline
**M07** & 89.56 & 89.98 & 90.32 & 89.95 \\ \hline
**M08** & 91.96 & 92.05 & 92.25 & 92.09 \\ \hline
**M09** & **94.32** & **94.1** & **95.01** & **94.48** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Sentiment classification accuracy (%) results using the MFSC model. Figure 5: Graphical representation of the performance results with the MFSC model. MFSM with BiLSTM and BERT: This is a sentiment categorization approach based on the BERT, BiLSTM, and MFSM models. To begin, in the dataset, language symbols and emoticon symbols are both treated as language symbols. The BiLSTM model collects sentiment characteristics from the dataset after initializing the word vector with BERT. Finally, a completely connected network is used to achieve sentiment categorization. ### Evaluation of Multi-Level Model of Sentiment Classification (MLSC)

In the second phase of the performance evaluation of the proposed model, the evaluation is conducted only with the multi-dimension model of sentiment classification (MLSC). The MDSC model with XLNet is compared with the CNN with Word2 Vec, BiLSTM with Word2 Vec, CNN with BERT, and BILSTM with BERT approaches, as shown in Table 13 and Figure 6. In addition to these models, the MDSC model is also implemented with the abovementioned techniques. MLSC with CNN and Word2 Vec: The classification of sentiments is accomplished with the assistance of the Word2 Vec, CNN, and MLSM models.