Aspect-Based Sentiment Analysis
Overview
Aspect-Based Sentiment Analysis studies how to find opinion targets—such as food, service, or battery life—and link them to sentiment, opinion terms, or explanations. Common tasks include aspect and opinion span extraction, triplet and quadruple prediction, implicit aspect identification, and valence-arousal scoring. Methods range from fine-tuned Transformer encoders, sequence labeling, and syntax or graph models to prompted or fine-tuned large language models, often supported by synthetic data, verification, or distillation. A central challenge is making structured predictions reliable across domains and languages while reducing annotation and inference costs, especially where labeled data are scarce.
Latest papers
52 papers4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.