# BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords
## 4 Experiments and Analysis
### Experiment Analysis  
To further evaluate the proposed BERT-BiLSTM OCBs sentiment analysis model, we set up eight sets of experiments. The values of precision, recall, and F1 are obtained on the test set, respectively. The comparison results of the constructed OCBs dataset are shown in Table 3.  
According to Table 4, the results of the proposed BERT-BiLSTM model on the OCBs dataset are better than those of the other models. First, the F1-score of the BERT-BiLSTM model is the highest, which means that the model has strong comprehensive ability and good generalization performance. Second, the recall value of the BERT-BiLSTM model is also the highest compared to other models. This also shows that the number of positive samples identified by the model is the best, and the coverage of training samples is wide. It reflects the sensitivity of the model. Third, in terms of precision, BERT-BiLSTM performs well. Its value is close to BERT, slightly (only 0.54%) worse than BERT-CNN and BERT-RNN. This is due to the high recall rate of the model, which indirectly leads to a decrease in the accuracy rate. Notably, the ability of the BiLSTM model to learn text context features is more prominent, so the results of the three models, BERT-BiLSTM, BiLSTM, and BiLSTM-Attention, are better in terms of recall and F1-score values. The BERT-BiLSTM model has the best training results. This proves that the word vector obtained by using the BERT pre-training model contains more complete context information, which is conducive to extraction of text information by the subsequent model. Figure 8 demonstrates the comparison of F1 score in the eight sets of experiments. The F1-score of the BERT-BiLSTM model is the highest among the eight sets of comparative experiments. It also shows the improvement in the overall classification performance of the model and the improvement in the ability to distinguish different categories, which proves that the model is performing strong emotional analytical skills. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline ERNIE [36] & 94.20 & 93.28 & 93.74 \\ BiLSTM–Attention [37] & 93.59 & 93.63 & 92.61 \\ BiLSTM [37] & 92.05 & 92.96 & 92.50 \\ TextCNN [38] & 92.83 & 91.77 & 92.29 \\ BERT–BiLSTM & 93.78 & **93.80** & **93.79** \\ BERT–CNN & **94.32** & 92.81 & 93.56 \\ BERT–RNN & 94.27 & 93.00 & 93.63 \\ BERT [30] & 93.86 & 93.55 & 93.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Result** & **Positive** & **Negative** \\ \hline Guessed pos & TP & FP \\ Guessed neg & FN & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation indicators. good results. In order to maintain fairness, the parameters are consistent with the previous experiments. The three models are as follows:  
* BERT: The word vector output from BERT pre-training model is used to calculate the emotional polarity through Softmax (a traditional classifier). * BiLSTM: The OCBs vector is generated through word2 vec (a typical method of word embedding), then the generated word vector is input into the BiLSTM model for feature extraction, and then emotional classification is conducted through Softmax (a traditional classifier). * BERT-BiLSTM: By fine-tuning the BERT model, the dynamic representation of the OCBs vector is generated in the downstream task, then the generated word vector is input to the BiLSTM model for feature extraction, and then the emotional classification is conducted through Softmax (a traditional classifier). In order to prove the superiority of proposed BERT-BiLSTM model, ablation experiments are conducted on the OCBs dataset (as Table 5). First, the results show that the model has a more obvious boosting effect in terms of recall and F1 values.