Sentiment Dependency Learning

Sentiment Dependency Learning is an advanced technique in the field of natural language processing, aimed at accurately capturing and understanding the sentiment orientation of text by analyzing the dependency relationships between sentiment words and their context. This method leverages the dependency syntax tree structure to deeply explore the semantic connections between sentiment words and other words, thereby enhancing the accuracy and reliability of sentiment analysis. Its application value is extensive, including public opinion monitoring, user feedback analysis, market trend prediction, etc., providing valuable decision support for enterprises and individuals.