News Public Opinion Analysis and Prognostication System Grounded on Massive Data and Artificial Intelligence

This paper proposes a news public opinion analysis system based on big data and artificial intelligence to address the issues of news public opinion analysis and prediction. The system employs a deep neural network model. In this model, the word vector model converts text into vector representations, and the Long Short-Term Memory network (LSTM) is used to capture the long-term dependencies in text sequences. Massive news data is collected based on the big data platform and undergoes preprocessing such as cleaning and annotation to ensure high-quality data. It achieves in-depth feature extraction and semantic understanding of public opinion information. The results presented that our new system has an accuracy rate of 85% in the emotional classification task and 90% in the topic classification task. This system can analyze and predict news public opinion efficiently and accurately, providing strong support for public opinion monitoring and decision-making in many institutions, and has good application prospects.