Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

The document discusses a supervised ranking approach for sentiment analysis that focuses on targeting sentiment expressions and their associated targets in text. It analyzes previous work, outlines a method for learning from annotated data, and presents evaluation results demonstrating the efficacy of their method compared to baseline proximity approaches. The conclusion indicates that the proposed method significantly improves performance by incorporating syntactic and semantic features.