# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 7 Discussion  
In this study, we constructed a method using a sentiment dictionary + RoBERTa-BiLSTM-Attention model for the sentiment classification of public opinion comments and selected the public opinion event of a university in Jiangxi as a case to verify the superior performance of the model in sentiment classification. Finally, based on the sentiment classification dataset, we analysed the trend of public opinion volume and the evolution process of sentiment to provide support for opinion guidance decision-making. In terms of data privacy and anonymity, this study took the following measures to protect personal information during data collection and processing. During the data collection process, data were collected only from public social media platforms (e.g., Weibo), and all data were comments publicly posted by users. During data processing, user information was anonymised, and only user comment data were used, removing all information that could identify individuals, such as usernames and IP addresses. This study followed relevant ethical norms to ensure that all data were used in accordance with laws and regulations and platform policies. However, this study has some limitations. The data for the study mainly came from a microblogging platform, and future research could consider integrating multiple data sources such as WeChat and news platforms to obtain more comprehensive public opinion information. In terms of the model's ability to generalise, as this study's approach to constructing the supplementary lexicon was event-specific, the sentiment lexicon was able to work effectively across different domains through appropriate adaptations and extensions. The deep learning model RoBERTa-BiLSTM-Attention did not show overfitting or underfitting for this opinion event, which indicates that the model's generalisation ability is good. However, its generalisation ability has not been fully validated for different types of food safety events; thus, the model can be tested across events to assess and improve its generalisation ability. In addition, the computational complexity and training time of the model need to be further enhanced, and, in the future, mixed-precision training can be used to reduce memory usage and accelerate training by dynamically adjusting the precision of the data during the training process. It is also possible to use the parallel training of models on multiple GPUs to significantly reduce the training time. Future research can further deepen the understanding of the evolution of public opinion events and provide more comprehensive and effective strategies for public opinion management. J.D. : Investigation, Writing--Reviewing and Editing, Visualisation, Formal Analysis. Y.L. : Conceptualisation, Methodology, Software, Writing--Original Draft. All authors have read and agreed to the published version of the manuscript. This paper contains some of the research results for the National Natural Science Foundation of China's major project 'Privacy Data Protection and Medical Risk Decision Making in the Environment of Medical Network' (project no. 72293583), the Ministry of Education of China's Philosophy and Social Science Research Major Project 'Risk Governance of Generative Artificial Intelligence Systems and Capacity Building', the Jiangsu Provincial Social Science Foundation Key Project/Social Science Association Major Project (project no. 24 WTA-008), the Ministry of Education project 'Risk Governance and Capacity Building of Generative Artificial Intelligence Systems' (project no. 72293583), and the Key Project of Jiangsu Provincial Social Science Foundation/Major Project of the Social Science Federation (project no. 24 WTA-008). Not applicable. The raw data supporting the conclusions of this article will be made available from the corresponding author upon reasonable request.