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Ethical governance of science and technology and sustainable development in the digital age: An exploration of intelligent administration using neural network algorithms

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-education
data-mining-and-machine-learning
software-engineering

Abstract

This manuscript outlines the application of public sentiment monitoring and analytical methodologies within the context of intelligent governance to enhance the quality and responsiveness of governmental services. Concomitant with the precipitous evolution of informational technologies, significant strides have been made in the realms of natural language processing (NLP). This research employs a sophisticated BERT-Transformer model to conduct nuanced sentiment analysis of textual opinions, amalgamating BERT's pre-training mechanisms with the Transformer's adeptness at context interpretation. The process commences with the BERT model's pre-training feature encoding the textual data, followed by the Transformer's multi-attention mechanism refining the extraction and comprehension of sentiment tendencies, culminating in the sentiment polarity's precise determination via a classification layer. This technique not only heightens the model’s sensitivity to shifts in textual sentiment but also bolsters the precision and efficiency of the analysis. Evaluations conducted on various public and bespoke datasets reveal that this model substantially outperforms conventional BERT models and other deep learning-centric sentiment analysis strategies in sentiment polarity classification tasks, elevating accuracy from 0.809 to 0.913 on bespoke datasets. By meticulously parsing and evaluating public sentiment, this investigation furnishes robust decision-making support for governmental entities, fostering the customization and sophistication of public services. The adoption of the proposed BERT-Transformer model within intelligent administrative frameworks empowers a more nuanced understanding of public exigencies and refines policy interventions, thereby enhancing the overall quality of public services and elevating public contentment. Furthermore, this study offers vital technical insights and a pragmatic foundation for the prospective advancements in text sentiment analysis technologies.
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