Design of Large-Scale Public Sentiment Analysis System Based on Transformer Architecture

This study aims to explore the design of large-scale public opinion sentiment analysis system based on Transformer architecture. By using Transformer architecture, sentiment analysis, data mining and text classification, an innovative sentiment analysis system is proposed to improve the accuracy and efficiency of sentiment analysis. The Transformer architecture is used to analyze large-scale public opinion data through training model, so as to predict and identify the emotional tendency of public opinion. By analyzing public opinion data, we can realize the early detection and identification of emotional tendency by using sentiment analysis method. The data mining technology monitors the public opinion data in real time to realize the real-time tracking of the emotional changes of public opinion. Finally, using the method of text classification, the public sentiment is classified, which provides an effective solution for the analysis of public sentiment. This system provides an efficient and accurate solution for large-scale sentiment analysis, helps to timely understand public sentiment, and provides support for public sentiment management.