## 4 Experiments
### Experiment 2: Training the Model Using a Small Amount of Data from SMP2020-EWECT  
In order to more fully investigate the effect of a small amount of data on the model accuracy and F1 values, we took out 1, 2, 4, 8, 16, and 32 pieces of data for each of the 6 sentiments, forming 6 small-sample data sets. The prediction accuracy and Macro-F1 were obtained by training the 6 small-sample datasets and obtaining predictions on the test set.  
To improve the accuracy of our model, we used a fine-tuning scheme to fine-tune our model Bert encoder and some parameters using the ccf_tcci_2013 and ccf_tcci2018 datasets.  
The results of the general dataset experiment are shown in Table 6. The experimental data of the COVID-19 Weibo dataset are shown in Table 7. The experimental results show that our model achieves the highest accuracy and Macro-F1 values on every small dataset, outperforming the mainstream deep learning models and the latest sentiment analysis models. This shows that our model is effective. Our model achieves very good results over both the general dataset and the COVID-19 Weibo dataset, with the highest correctness and Macro-F1 values. In the COVID-19 Weibo dataset, our model also maintains the highest correct rate, and the Macro-F1 value is 0.019 higher than that of our model for a single sentiment with 32 data. However, the correct rate is 0.064 lower than ours, and the change curve of our model's correct rate and Macro-F1 value indicate that our model shows less fluctuation compared to the other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**16**} & \multicolumn{2}{c}{**32**} \\  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline RCNN & 0.294 & 0.083 & 0.379 & 0.194 & 0.285 & 0.242 & 0.379 & 0.366 & 0.377 & 0.370 & 0.498 & 0.439 \\ TextCNN & 0.160 & 0.108 & 0.430 & 0.316 & 0.259 & 0.125 & 0.320 & 0.278 & 0.394 & 0.291 & 0.406 & 0.409 \\ Bi-LSTM & 0.218 & 0.084 & 0.187 & 0.076 & 0.223 & 0.127 & 0.324 & 0.267 & 0.240 & 0.176 & 0.331 & 0.313 \\ Bi-LSTM-Attention & 0.210 & 0.058 & 0.310 & 0.187 & 0.238 & 0.148 & 0.259 & 0.192 & 0.219 & 0.216 & 0.362 & 0.270 \\ BERT-Bi-LSTM & 0.293 & 0.076 & 0.277 & 0.185 & 0.333 & 0.319 & 0.451 & 0.421 & 0.433 & 0.394 & 0.520 & 0.462 \\ BERT-Bi-LSTM-atten & 0.315 & 0.209 & 0.359 & 0.236 & 0.480 & 0.358 & 0.438 & 0.393 & 0.488 & 0.463 & 0.555 & 0.423 \\ BBMEJ-BTAFT & 0.339 & 0.270 & 0.448 & 0.282 & 0.586 & 0.439 & 0.593 & 0.480 & 0.621 & 0.488 & 0.629 & 0.521 \\ \hline \end{tabular}
\end{table}
Table 6: Training results for small samples of the general dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**200**} & \multicolumn{2}{c}{**2000**} \\  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline RCNN & 0.483 & 0.136 & 0.432 & 0.193 & 0.626 & 0.474 & 0.738 & 0.582 \\ TextCNN & 0.165 & 0.106 & 0.508 & 0.126 & 0.657 & 0.525 & 0.702 & 0.556 \\ BiLSTM & 0.134 & 0.110 & 0.440 & 0.222 & 0.620 & 0.453 & 0.714 & 0.544 \\ BiLSTM-Attention & 0.427 & 0.159 & 0.444 & 0.255 & 0.625 & 0.469 & 0.721 & 0.543 \\ Bert-BiLSTM & 0.387 & 0.189 & 0.525 & 0.316 & 0.619 & 0.447 & 0.747 & 0.591 \\ Bert-BiLSTM-Attention & 0.469 & 0.165 & 0.555 & 0.364 & 0.645 & 0.508 & 0.747 & 0.596 \\ BBMEJ & 0.423 & 0.171 & 0.544 & 0.420 & 0.626 & 0.482 & 0.751 & 0.616 \\ \hline \end{tabular}
\end{table}
Table 5: Experimental results of the SMP2020-EWECT COVID-19 Weibo dataset.