Model Framework for Discovering and Utilizing Public Opinion Hot Topic Knowledge in the Social Media Network Environment

The quick dissemination and nuanced nature of public opinion present additional difficulties for public opinion analysis in the age of social media's information explosion. Traditional public opinion analysis methods suffer from insufficient processing capabilities and single analysis methods, making it difficult to cope with large-scale and rapidly growing social media information. This article aims to utilize social media data sources and advanced algorithm models such as TextCNN (Text Convolutional Neural Network) and LSTM (Long Short-Term Memory) to construct a comprehensive model framework that addresses the limitations of traditional public opinion research and improves the accuracy, timeliness, and systematicity of public opinion hot topic knowledge discovery and utilization, thereby providing scientific basis for decision-making and optimizing the decision-making process.