###### Abstract  
The issue of aquatic product quality and safety has gradually become a focal point of societal concern. Analyzing textual comments from people about aquatic products aids in promptly understanding the current sentiment landscape regarding the quality and safety of aquatic products. To address the challenge of the polysemy of modern network buzzwords in word vector representation, we construct a custom sentiment lexicon and employ the Roberta-wwm-ext model to extract semantic feature representations from comment texts. Subsequently, the obtained semantic features of words are put into a bidirectional LSTM model for sentiment classification. This paper validates the effectiveness of the proposed model in the sentiment analysis of aquatic product quality and safety texts by constructing two datasets, one for salmon and one for shrimp, sourced from comments on JD.com. Multiple comparative experiments were conducted to assess the performance of the model on these datasets. The experimental results demonstrate significant achievements using the proposed model, achieving a classification accuracy of 95.49%. This represents a notable improvement of 6.42 percentage points compared to using Word2 Vec and a 2.06 percentage point improvement compared to using BERT as the word embedding model. Furthermore, it outperforms LSTM by 2.22 percentage points and textCNN by 2.86 percentage points in terms of semantic extraction models. The outstanding effectiveness of the proposed method is strongly validated by these results. It provides more accurate technical support for calculating the concentration of negative emotions using a risk assessment system in public opinion related to quality and safety. a +
Footnote †: journal: Medical  
emotions, in establishing this system. Technical support for calculating the concentration of negative sentiments can be provided according to accurate analysis and prediction of these emotional tendencies. Consequently, this serves as a basis for the assessment of public opinion on risks related to product quality and safety. In the field of natural language processing, sentiment analysis is a crucial research area. However, traditional sentiment analysis methods often fail to capture the semantic information and context relevance of a text adequately. Reduced accuracy and stability of sentiment analysis results can result from this limitation [3]. To overcome these challenges, researchers have increasingly turned their attention to sentiment analysis models based on deep learning. In the past few years, research on text sentiment analysis has garnered widespread attention and significant progress in the field of natural language processing. An overview of the current status and key achievements of some relevant research is provided in the following. Research based on traditional methods: Dictionaries and machine learning methods are primarily utilized in traditional studies on text sentiment analysis. Sentiment dictionaries, in particular, are commonly used tools that classify sentiment by associating words with emotional polarities. For instance, [PERSON] et al. [4] proposed a sentiment lexicon called \"Opinion Lexicon\" for sentiment analysis of text. However, substantial human resources are required for the establishment and maintenance of sentiment lexicons, and they lack strong scalability. Moreover, conventional machine learning algorithms like Support Vector Machines (SVMs) and Naive Bayes have been extensively used in sentiment classification tasks [5]. For example, [PERSON] et al. [6] utilized the SVM method for sentiment analysis on texts from Twitter. These methods excel in powerful multi-feature modeling but require manual feature engineering, making model generalization challenging. Research based on deep learning methods: In recent years, deep learning methods have achieved significant progress in the study of text sentiment analysis. Models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) are widely used to model sequential information in text.