Design and Implementation of a Public Opinion Network Analysis System Based on Derived GANs
-- A Case Study of Trump Tariff-Related Public Opinion
DOI:
https://doi.org/10.54097/q52r1635Keywords:
Component, Public Opinion Analysis, GAN, Large Language ModelsAbstract
This thesis addresses the complex public opinion monitoring challenges arising from the Trump administration's tariff policies by proposing an innovative public opinion analysis framework based on Generative Adversarial Networks (GANs). The framework integrates multiple specialized GAN models (e.g., T-GAN, Senti-GAN, CM-GAN) to tackle issues such as data scarcity, sensitive information deletion, and insufficient multimodal fusion in traditional public opinion monitoring. By combining GAN-generated data with the RoBERTa-BiLSTM-Multihead Attention (RBMA) large language model, the system is expected to significantly outperform traditional unsupervised methods and purely generative models in tasks such as term recognition, stance classification, and risk warning. This architecture not only reduces the dependency on and cost of annotated data but also exhibits high robustness, providing an efficient and reliable solution for public opinion monitoring amid political and economic conflicts such as tariff wars.
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