Campus network public opinion monitoring method based on emotional feature extraction and classification

Key Points

• The aim is to develop a method for monitoring public opinion on campus networks using emotional features.
• Implemented emotional feature extraction techniques
• Utilized classification algorithms for data analysis
• Employed a network analysis framework to assess public sentiment
• Emotional feature extraction improved accuracy in public opinion classification by 30%
• Classification algorithms achieved a 98% accuracy rate in identifying sentiment
• Significant correlation found between emotional features and public opinion trends