Research on the Method of Analyzing Topic‐Sentiment Public Opinion Based on Complex Networks

The proliferation of the Internet and social networks has significantly expanded the user base of media platforms, making the management of public opinion on these platforms a critical concern. Previous approaches have predominantly examined topics and sentiments either independently or jointly, often limiting the analysis to textual features alone. To address this limitation, this paper proposes a multidimensional “topic‐sentiment‐network” (TSN) method and develops a three‐layer architecture for public opinion analysis. The method integrates BERTopic and SnowNLP for joint topic‐sentiment analysis and constructs complex networks to reveal the structural characteristics of opinion propagation. Finally, a simulation experiment is conducted on the microblog topic “DeepSeek suffering from US‐based brute‐force cyberattacks.” Comparative evaluations demonstrate that the proposed framework achieves superior performance, with a Topic Diversity score of 0.975 and a sentiment analysis accuracy of 71.6%. Furthermore, an ablation study confirms that the sentiment‐weighted network structure improves modularity by 4.2% (from 0.2600 to 0.2708) compared to traditional topology‐based networks. These results indicate that this method provides a systematic, comprehensive, and quantitative framework for monitoring public opinion dynamics.