###### Abstract
For instance, [PERSON] et al. [7] proposed a model based on Convolutional Neural Networks (CNNs) for sentiment classification tasks. Additionally, the LSTM model was introduced by [PERSON] et al. [8] to address the challenge of capturing long-distance dependencies. These methods exhibit an excellent contextual understanding and possess the ability to automatically learn features from a text. Complex semantic structures are more easily comprehended using methods of this kind. However, these methods rely on robust word embedding models. The semantic and contextual relationships in vocabulary may be effectively captured using traditional word embedding models, such as Word2 Vec, which are mostly unidirectional. Research based on pre-trained language models: The emergence of pre-trained language models has brought new opportunities to the study of text sentiment analysis. [PERSON] et al. [9] introduced the BERT (Bidirectional Encoder Representations from Transformers) model, achieving excellent performance on multiple sentiment classification datasets. BERT is a pre-trained language model based on the Transformer architecture. A Transformer is an architecture that employs an attention mechanism. The central concept of a Transformer is to capture dependencies in sequential data using self-attention and positional encoding. This results in sequential data being processed more effectively compared to using the traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). The Transformer architecture has been widely applied in various fields. For example, in the field of computer vision (CV), Transformers are used for tasks such as image classification, object detection, and image generation [10]. In the field of speech processing, Transformers are used for speech recognition and speech synthesis [11]. Additionally, Transformers are also applied in recommendation systems, time-series analysis, and bioinformatics. BERT represents a significant application of the Transformer architecture in the field of natural language processing (NLP). The encoder part of the Transformer is utilized by BERT to learn bidirectional language representations. The innovation of BERT lies in its pre-training tasks,which mainly include the Masked Language Model (MLM) and Next Sentence Prediction (NSP). BERT pre-trains on a large number of text data. As a result, rich language features can be learned. These features can be transferred to downstream natural language processing (NLP) tasks. Examples of such tasks include text classification, named entity recognition, and question-answering. The performance of these tasks is significantly improved by this transfer [12]. However, as a model for text sentiment analysis, BERT has high data and computational resource requirements, making it less suitable for small-scale applications. Roberta (A Robustly Optimized BERT Pretraining Approach) was introduced by Facebook AI in 2019 as a significantly improved pre-training language model based on BERT [13]. By conducting thorough optimizations and improvements on the BERT architecture, Roberta successfully achieved superior performance. This improvement in Roberta is mainly attributed to a series of intelligent training strategies and carefully tuned hyperparameters. The model's representational and generalization capabilities are further boosted by these enhancements, leading to outstanding performance in various natural language processing tasks. On the other hand, a bidirectional long short-term memory (BiLSTM) model is a classic recurrent neural network that effectively captures the temporal dependencies in text sequences [14]. Currently, there is limited research on sentiment analysis of specific aquatic product review data, and the methods mostly used are the aforementioned general sentiment analysis methods. These methods include traditional approaches, deep learning techniques, and methods based on pre-trained language models.