Structured Sentiment Analysis

Structured sentiment analysis (SSA) aims to extract detailed sentiment information from text, going beyond simple positive/negative classifications to identify the entities involved (e.g., holder, target) and their relationships. Current research focuses on improving the accuracy and efficiency of SSA using various approaches, including graph-based parsing, contrastive learning, and transformer-based models like BERT, often incorporating minimalist tagging schemes for enhanced performance and reduced computational cost. Advances in SSA have significant implications for applications requiring fine-grained sentiment understanding, such as opinion mining, brand monitoring, and customer feedback analysis.