Research on the Semantic Information Extraction Technology Model System in the Informatization of Online Public Opinion

This paper proposes a semantic information extraction technology model system based on BERT, aiming to solve the problem of semantic understanding and information extraction in online public opinion analysis. The system design includes data collection, preprocessing, semantic information extraction and result display modules, using the BERT model to capture the deep semantic relationship of the text and enhance the ability to understand complex semantics and contextual information. The data collection module covers multiple sources such as social media, news comments and forum posts, and ensures the high quality and diversity of data through real-time crawling and cleaning. The semantic information extraction module is based on the multi-head attention mechanism of BERT, which can effectively capture the emotional tendency, topic information and potential intention in the public opinion text. The experiment verifies the system performance based on 1 million online public opinion data. The results show that in the semantic information extraction task, the accuracy of the BERT-based model is improved by 22.4%, the recall rate is improved by 19.8%, and the F1 value reaches 91.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, which is significantly better than traditional methods such as TF-IDF and Word2Vec. The sentiment classification module has an accuracy of 93.1%, achieving accurate recognition of positive, negative and neutral emotions. The processing time of a single piece of public opinion text is reduced by an average of 25.6%, indicating the high efficiency of the system in large-scale data processing. The simulation test verifies the system's stable operation capability in the scenario of 100,000 new public opinion data per day, with a delay of less than 1 second, which can meet the needs of real-time public opinion analysis.