Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
This addresses the challenge of domain generalization in aspect-based sentiment analysis for applications lacking annotated datasets, though it is incremental as it builds on existing unsupervised methods.
The paper tackles the problem of extracting aspect-sentiment tuples in low-resource domains by proposing an unsupervised approach, achieving competitive performance on four benchmark datasets without labeled data.
Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions. Conventional methodologies predominantly rely on supervised approaches; however, the efficacy of such methods diminishes in low-resource domains lacking labeled datasets since they often lack the ability to generalize across domains. To address this challenge, we propose a simple and novel unsupervised approach to extract opinion terms and the corresponding sentiment polarity for aspect terms in a sentence. Our experimental evaluations, conducted on four benchmark datasets, demonstrate compelling performance to extract the aspect oriented opinion words as well as assigning sentiment polarity. Additionally, unsupervised approaches for opinion word mining have not been explored and our work establishes a benchmark for the same.