###### Abstract
However, in sentiment analysis of aquatic product review datasets, these methods may encounter some disadvantages. Firstly, aquatic product reviews contain specific industry terms, domain knowledge, and the expression of sentiments. General sentiment analysis models may struggle to accurately capture the semantics and sentiments within them. Additionally, people may use various expressions to convey emotions when reviewing aquatic products, including a plethora of internet slang, making it challenging for sentiment analysis models to accurately capture all emotions. In addressing the advantages and disadvantages of the aforementioned methods, the current study further explores a combination of sentiment lexicons, pre-trained language models, and deep learning networks. A text sentiment analysis model is proposed based on the integration of a sentiment lexicon and a deep learning network. This study aims to utilize textual data related to aquatic products to build a sentiment classification model for training and evaluation. In conclusion, sentiment analysis research encompasses traditional methods, deep learning approaches, and methods based on pre-trained language models. The modeling capabilities for sentiment classification tasks have been significantly enhanced by the advent of pre-trained language models such as BERT. Based on the above, we chose a variant of the pre-trained BERT model, Roberta-wwm-ext, as the foundational word embedding model. Roberta-wwm-ext is based on the Transformer architecture and has been pre-trained according to extensive unsupervised learning, acquiring rich language representations. It has demonstrated excellent performance across various Chinese natural language processing tasks. The capability to better comprehend contextual information in text is possessed by Roberta-wwm-ext. By considering the position of words in a sentence or text and their relationships with surrounding words, it allocates more expressive vector representations to each word. This enables the model to capture semantic information in Chinese text more effectively, leading to improved performance across various natural language processing tasks. Roberta-wwm-ext is specifically trained for the Chinese context, taking into account the characteristics and structures of the Chinese language. Therefore, superior performance in understanding semantic and syntactic structures is exhibited by it when processing Chinese text. To address the challenge of the internet slang in online platform data, which can be difficult for models to understand, we incorporated a sentiment lexicon. Combining the Roberta-wwm-ext model with a sentiment lexicon allows for more accurate extraction of semantic information from text. The current study further explores the combination of sentiment lexicons, pre-trained language models,and deep learning networks. A text sentiment analysis model is proposed based on the integration of a sentiment lexicon and a deep learning network. The aim of this study is to build a sentiment classification model for training and evaluation using textual data related to aquatic products. The emotional analysis performance of the sentiment lexicons and deep learning models is assessed according to analysis of the experimental results. A comparison with traditional methods is conducted to validate the effectiveness and superiority of the proposed model in sentiment analysis of textual content related to the safety of aquatic products. The model provides technical support for calculating the concentration of negative emotions, thereby serving as a basis for assessing the public sentiment on risks related to the safety of aquatic products. The main contributions of the work in this paper are as follows:  
1. Firstly, a dataset of consumer reviews regarding the safety of aquatic product quality was established. This dataset is a valuable resource for sentiment analysis. It contains consumer opinions concerning the safety of aquatic products.