## 3 Experiment and Analysis
### Experimental Parameter Setting  
To fully demonstrate the effectiveness of the proposed method in our research, comparative experiments involving various widely used sentiment analysis models were conducted by us. This series of comparative experiments aims to intuitively validate the reliability and proficiency of the proposed method in terms of the network performance. In the experiments, the word embeddings generated by the pre-trained model Roberta-wwm-ext were utilized by us to endow the model with powerful semantic representation capabilities. During the model training process, we opted for the adaptive moment estimation (Adam) algorithm as the optimizer to expedite the convergence of training and enhance the overall training efficiency. Detailed hyperparameter tuning was conducted to obtain the best experimental results. These parameters included the number of training epochs, batch size, learning rate, the number of hidden units in LSTM (HiddenSize), and random dropout of hidden units during training. The objective of this process is to ensure that the model achieves optimal performance on the dataset used in this study. The term \"epoch\" refers to the number of times the entire training dataset is used to train the model. In each epoch, the model traverses the entire training dataset, adjusting its weights and parameters using an optimization algorithm (such as gradient descent) to minimize the training loss. Typically, training a model involves multiple epochs to ensure that the model adequately learns the features of the data and improves its performance. However, using too many epochs may lead to overfitting, where the model performs well on the training data but poorly on unseen data. Taking into account the dataset used in this study and the selected model, the number of epochs for the experimental model in this study was set to 6, aiming for a balance between capturing the patterns in the data and avoiding overfitting. The \"batch size (Batchsize)\" defines the number of samples used to update the model weights in each training step. When training neural networks, the entire training dataset is typically divided into small batches, with each batch containing a certain number of training samples. Batchsize for the model in this study was set to 16. This decision was based on considering the computational resources available in the experimental environment, as well as the size and characteristics of the dataset used in this study. The learning rate is used to control the magnitude of the parameter adjustments during each update step of the model. It determines the size of the step taken when updating the parameters along the gradient direction. For the model in this study, the learning rate was set to \(1\times 10^{-5}\). The number of hidden units (Hidden Size) and the random dropout of hidden units (Dropout) are crucial parameters affecting the performance and training process of long short-term memory networks. For the model in this study, Hidden Size was set to 320, and Dropout was set to 0.5. While BiLSTM models offer advantages in capturing the temporal dependencies in text sequences, they are not without challenges. One of the primary issues associated with BiLSTM models is the risk of overfitting, especially when dealing with small datasets. Overfitting occurs when the model learns the training data too well, including the noise, leading to poor generalization on unseen data. We carefully selected the optimal Dropout parameter according to multiple experiments to mitigate the risk of overfitting. Additionally, BiLSTM models are prone to vanishing and exploding gradient problems, although to a lesser extent than traditional RNNs. These issues can hinder the models' ability to learn long-range dependencies effectively. We addressed these issues by employing gradient clipping and meticulously initializing the model parameters.