Public opinion topic modeling and online public opinion governance using LDA algorithm

Abstract

The absence of effective dynamic tracking mechanisms in public opinion analysis has persistently constrained the accurate identification of evolving trends in public opinion events. This study proposes an innovative methodological framework that synergistically integrates the Latent Dirichlet Allocation (LDA) algorithm with advanced topic modeling techniques, thereby offering transformative potential for the analysis and governance of online public discourse. The methodology involves a sophisticated multi-stage analytical process: First, textual data undergoes rigorous preprocessing before being transformed into a term frequency matrix using Term Frequency-Inverse Document Frequency (TF-IDF) weighting, establishing a robust quantitative foundation for subsequent analysis. The LDA algorithm is then systematically applied to extract latent thematic structures from the dataset, while an original hierarchical optimization strategy substantially enhances the model’s dynamic topic identification and tracking capabilities. Furthermore, the integration of a sentiment lexicon with the extracted topics enables precise opinion classification, permitting real-time monitoring of topic evolution through quantitative heat fluctuation metrics. Empirical evaluations demonstrate the superior performance of the proposed approach compared to conventional LDA, achieving a 12.7% reduction in the average Root Mean Square Error (RMSE) for tracking topic heat dynamics. This methodological breakthrough not only advances the theoretical foundations of public opinion analysis but also provides a scientifically rigorous, dynamic monitoring framework with significant practical implications for evidence-based governance and policy formulation in the digital era.