## 3 Experiment and Analysis
### Comparing Model Settings  
The method of combining sentiment lexicons with deep learning has been studied for other types of review data. However, similar research has not been conducted on aquatic product review datasets. The sentiment lexicon used in this paper is independently constructed and differs from those used in other studies. Regarding the settings of comparative experiments, this paper uses generic text sentiment analysis methods and divides the experiments into two parts: text word vector extraction and text semantic extraction. The comparative experiments aim to demonstrate the superiority of the proposed word embedding model and the advantages of the text semantic extraction model used in this paper. Therefore, we select commonly used word embedding models such as Word2 Vec, BERT, and Roberta-wurm-ext. Additionally, generic text sentiment analysis models with good performance in neutral text sentiment research are chosen, including RNN, textCNN, LSTM networks, and BiLSTM. Different combinations of word embedding models and semantic extraction models are used to obtain different comparative experimental models. Through the design of comparative experiments, we aim to demonstrate the advantages of our method for the aquatic product review data used in this paper. Therefore, in this paper, our model is labeled \"our method\", and the comparative experimental models are as follows:  
1. Word2 Vec-RNN [21]: The model utilizes Word2 Vec as the word embedding model to obtain word vectors. Subsequently, it employs an RNN for feature training and classification. 2. Word2 Vec-LSTM [22]: Word2 Vec is utilized by the model as the word embedding model to obtain the word vectors. It employs LSTM for feature training and classification, with LSTM configured as two layers and the number of units in the hidden layer set to 320. 3. Word2 Vec-BiLSTM [23]: The model utilizes Word2 Vec as the word embedding model to obtain word vectors and represent the text features. Subsequently, it uses BiLSTM to process these features for classification. 4. Bert-RNN [24]: The input text is first encoded using the BERT model to obtain deep semantic representations of the text. Subsequently, these semantic representations serve as inputs to an RNN, which is responsible for processing the serialized semantic information and ultimately outputting the predicted sentiment category. 5. Bert-textCNN [25]: The model first encodes the input text using the BERT model to obtain contextual representation vectors for each word. These vectors are then used as inputs to the textCNN model. The textCNN model, through operations like convolutional and pooling layers, effectively extracts the key features from the text. The extracted features are put into a fully connected layer, and the output of the fully connected layer represents the final sentiment classification result. 6. Bert-LSTM [26]: BERT is first utilized by the model for feature extraction from the text. Subsequently, LSTM is employed to further process these features, and finally, a softmax classifier is used to classify the processed features. 7. Bert-BiLSTM [27]: The model utilizes a pre-trained BERT model by putting the text sequence into BERT to obtain contextual representations for each word. The output from BERT is then fed into a BiLSTM layer to further capture the sequential information in the text. Following the BiLSTM layer, a fully connected layer is added, and the final input to the softmax layer is used to classify the text into different sentiment categories. Roberta-wwm-ext-RNN: The text sequence is put into the pre-trained Roberta_wwm-ext model to obtain contextual representations for each word. Subsequently, an RNN is employed for feature training and classification. * Roberta-wwm-ext-textCNN: A model utilizing the pre-trained Roberta-wwm-ext model. The text sequence is put into this model to obtain contextual representations for each word.