Sentiment Analysis Based on Dependency Syntax and Deep Learning

Sentiment analysis is an important task in natural language processing (NLP), which aims to extract and identify emotional information from text. Traditional sentiment analysis methods rely on feature engineering, and the rise of deep learning techniques in recent years has brought new opportunities to the field. This paper proposes an emotion analysis model that combines dependency syntax and deep learning to improve the accuracy and interpretability of emotion classification. Through theoretical derivation and comparative simulation analysis, we verify the effectiveness of the proposed model and compare it with the traditional model.