## 4 Conclusions  
This paper initially examines the prevalent methods in general sentiment analysis. The specific challenges encountered by these methods in the specialized domain of textual sentiment analysis for aquatic product quality and safety are then highlighted. General sentiment analysis algorithms are unable to accurately identify the emotional tendencies of specialized domain vocabulary. Furthermore, the emotional tendencies within online popular vocabulary, derived from the rapid development of the internet, are unable to be accurately recognized using the traditional generic sentiment analysis methods. This study aims to tackle the aforementioned issues. It conducts a thorough comparative analysis of the current sentiment analysis models and selects Roberta-wwm-ext as the base word embedding model. Additionally, bidirectional LSTM is employed as the base semantic extraction model. Building on this foundation, we introduce a sentiment dictionary as an auxiliary tool. This sentiment dictionary is based on the Dalian University of Technology Chinese Sentiment Lexicon Ontology, to which we added 300 online popular vocabulary words and manually annotated their emotional tendencies. A sentiment dictionary is established in this paper to extract the emotional characteristics of vocabulary words. These emotional features are then concatenated with the base word embedding model. As a result, the proposed word embedding model can more accurately extract word vectors, which facilitates semantic feature extraction. We take into account the distribution of textual data on aquatic product quality and safety. Consequently, consumer reviews of aquatic products from real stores on the JD platform are selected as the base data. Various data processing operations were conducted, resulting in the establishment of an aquatic product quality and safety review dataset with 70,000 real comments and emotional labels. In future research, we will incorporate data from more platforms. This dataset can be made available to a wider audience for exploring sentiment analysis in the context of aquatic product quality and safety. The experimental analysis in this paper demonstrates that the model proposed in this paper exhibits clear superiority in performance based on the data presented in this paper. The proposed network in this paper achieves an accuracy rate of 95.49%, showing an improvement of 6.42 percentage points compared to using Word2 Vec as the word embedding model. Furthermore, the use of a sentiment dictionary to extract the emotional features resulted in a 1.44 percentage point increase in accuracy compared to not using a sentiment dictionary. The role of the sentiment dictionary in the word embedding model is fully demonstrated by this result. Moreover, this method can provide technical support for calculating the concentration of negative emotions. As a result, a basis for risk assessment according to public opinion related to aquatic product quality and safety can be provided by this method.  
Conceptualization, M.C. and G.F.; methodology, X.T.; software, X.T.; validation, M.C. and G.F.; formal analysis, M.C. and X.T.; resources, M.C.; data curation, M.C. and X.T.; writing--original draft preparation, M.C., X.T. and G.F.; writing--review and editing, M.C., X.T. and G.F.; supervision, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Development Planning in Key Areas of Guangdong Province, No. 2021B0202070001.  
Not applicable.  
The experimental data related to this paper can be requested from the authors by email. If a researcher is in need of the dataset, they can email [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.