The Application of Deep Learning in Social Media Sentiment Analysis and Public Opinion Prediction

Abstract

The rapid development of social media has generated a vast amount of unstructured text data, providing abundant resources for sentiment analysis and public opinion prediction. Deep learning, with its capabilities of automatic feature extraction, sequence modeling and multimodal fusion, has become the core technical path in this field. This paper systematically reviews the theoretical basis and application progress of deep learning in social media sentiment analysis and public opinion prediction, covering aspects such as sentiment classification, fine-grained recognition, real-time monitoring, trend prediction, crisis warning and cross-platform correlation analysis. It discusses key technologies such as pre-trained language models, attention mechanisms and knowledge graphs, and analyzes the current challenges and future development trends.