Xiangyao Ma
ABSTRACT
This study focuses on cross-platform online public opinion topic modeling and analysis, using popular topics on social media platforms, including Weibo and Zhihu. Through preprocessing, text embedding, dimension reduction, and clustering, we extract keywords related to the topics, and use generative artificial intelligence to convert topic representations into natural language. This study effectively analyzes the topic features implicit in user generated contents. Experimental results indicate that our method outperforms existing one and can effectively model online public opinion topics. The results were analyzed, and the experimental results indicated that proposed method can effectively model online public opinion topics, providing new insights for monitoring and analyzing online public opinion.
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Digital Library
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Index Terms
- Cross-Platform Network Public Opinion Topic Modeling
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