## 2 Materials and Methods  
### Data Acquisition  
The dataset used in this study is sourced from JD.com. JD.com is one of the largest e-commerce platforms in China with a diverse user base, representing a comprehensive spectrum of consumers. Review data from e-commerce platforms offer genuine evaluations from consumers after their purchases. These data are considered to provide the most authentic feedback on products. Analyzing reviews for a specific type of product on the entire e-commerce platform helps us understand the general public sentiment toward such products. Distinct characteristics are exhibited by this dataset compared to comment datasets on other products. Firstly, aquatic product comment datasets often involve knowledge from specialized fields such as water quality, water treatment technology, and aquaculture. Therefore, more professional vocabulary is contained in them compared to datasets for other products. Secondly, aquatic products are related to human health and safety. Therefore, comments on these products tend to focus more on aspects such as quality, safety, and freshness. These aspects may not be as prominent in comments on other products. Additionally, compared to other products, the production of aquatic products may be more susceptible to environmental factors such as water pollution and climate change. Hence, the dataset may involve more discussions on the impact of environmental factors on product quality and sustainability. Furthermore, as the dataset is sourced from an online platform, it is likely to contain a large amount of internet slang while possessing these characteristics. Based on the above, this dataset is used for the experiments in this study. Web scraping techniques were utilized to extract reviews of aquatic products from various stores on JD.com. Specifically, we collected positive reviews, labeled as 1, and negative reviews, labeled as 0, from different stores. The main focus of this paper is two types of aquatic products: consumer reviews of fresh shrimp from different stores and consumer reviews of salmon. The combined dataset comprises 127,349 reviews for the two products, including 86,325 positive reviews and 41,024 negative reviews. The user reviews on JD.com may reflect the opinions and preferences of specific user groups, rather than the entire audience. Some users may prefer shopping on JD.com, while others may prefer other e-commerce platforms. Less comprehensive evaluations of products or services may result from such biases. Additionally, the quality of reviews from different users may vary, with some reviews being overly subjective or lacking detailed information. Some reviews may be fake or manipulated by competitors, affecting the credibility of the data. In the obtained review dataset, there were default comments, which are automatically generated positive reviews given by the system when users did not provide timely feedback. Additionally, duplicate comments were present in the dataset. This is likely due to certain merchants engaging in fake positive reviews practices, which led to repetitive standardized positive comments. These default and duplicate comments lack substantive content and do not represent genuine consumer evaluations. As a result, they potentially influence the experiments. Therefore, we removed these comments. Furthermore, some short-length comments, most of which consisted of only one or two words, were filtered out. After undergoing the aforementioned steps, we finally selected 35,000 positive comments and 35,000 negative comments, totaling 70,000 comments, as the final experimental dataset. Some examples of comments in the dataset in this article are shown in Table 1.  
Due to the presence of numerous meaningless words and expressions in the comment dataset, after selecting the final dataset, the text data are initially processed by removing special characters and punctuation. Non-alphanumeric characters, punctuation, and other special characters are eliminated from the text.