## 3 Experiment and Analysis
### Network Training and Comparative Analysis of the Results  
The results of the model established in this paper, as well as the various comparative models set out in this paper, for the dataset are presented in Table 3.  
We compared our method with model 3 and model 7 to evaluate the effectiveness of our proposed approach using a custom sentiment lexicon and Roberta-wwm-ext as the word embedding model. Table 3 shows that our proposed embedding model outperforms the use of Word2 Vec by 6.42 percentage points and improves upon using BERT as the word embedding model by 2.06 percentage points. The text representation based on Word2 Vec shows the lowest performance across all metrics. This is mainly attributed to Word2 Vec's excellent representation of semantic relationships between words in the text but neglect of the issues related to polysemy in different contexts and long-distance semantic associations. To validate the effectiveness of bidirectional LSTM in feature extraction, we compared models 8-11. In this comparison, all the models use Roberta-wwm-ext as the word embedding model. In terms of the classification accuracy, bidirectional LSTM outperforms LSTM by 0.68 percentage points and surpasses textCNN by 1.42 percentage points. This demonstrates its superior performance and effectively verifies its ability to address the issues of vanishing and exploding gradients traditionally associated with RNN models during training. We compare model 11 with the method proposed in this paper to demonstrate the effectiveness of the sentiment dictionary designed and used in this study. Compared to the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Networks** & **Accuracy (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline Word2 vec-RNN & 87.60 & 86.58 & 87.01 \\ Word2 vec-LSTM & 88.75 & 88.48 & 88.62 \\ Word2 vec-BiLSTM & 89.07 & 88.83 & 88.96 \\ Bert-RNN & 92.59 & 92.25 & 92.48 \\ Bert-textCNN & 91.70 & 91.23 & 91.51 \\ Bert-LSTM & 92.63 & 92.27 & 92.46 \\ Bert-BiLSTM & 93.43 & 92.86 & 93.09 \\ Roberta-wwm-ext-RNN & 93.05 & 92.32 & 92.18 \\ Roberta-wwm-ext-textCNN & 92.63 & 92.01 & 92.44 \\ Roberta-wwm-ext-LSTM & 93.27 & 92.62 & 92.97 \\ Roberta-wwm-ext-BiLSTM & 94.05 & 93.36 & 93.72 \\ Our method & 95.49 & 94.53 & 95.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results between sentiment analysis networks. model used in this paper, model 11 eliminates the feature of using a sentiment dictionary to enhance the word vectors. The rest of the network structure is consistent with the network structure of this paper. The results in the table show that the accuracy of the model without the sentiment dictionary is 1.44 percentage points lower than that of the model with the sentiment dictionary. In terms of recall and F1 score, using a sentiment dictionary to extract features from some difficult-to-judge online popular words shows higher values. From this, we can conclude that on the same aquatic product review dataset, the model's performance can be improved, and the accuracy of sentiment orientation judgment can be enhanced by the addition of a sentiment dictionary. To visually observe the relative performance advantages of the proposed model compared to other networks on the dataset, as well as the convergence of the model, the changes in accuracy with respect to the step length for each model on this dataset are presented in Figure 5,b. From Figure 5, it can be observed that the proposed word embedding model outperforms BERT. The proposed word embedding model exhibits higher accuracy and a faster growth rate in the first epoch. As the number of training iterations increases, the model's accuracy gradually stabilizes. From Figure 5, it can be observed that bidirectional LSTM has advantages compared to other text sentiment analysis models. Although textCNN converges quickly, its accuracy is lower compared to the LSTM network. Bidirectional LSTM exhibits higher accuracy and a faster convergence speed than unidirectional LSTM.