AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
This addresses the problem of improving sentiment analysis accuracy for applications like review analysis, though it is incremental over existing BERT methods.
The paper tackled aspect-level sentiment classification by incorporating aspect-aspect relations from knowledge graphs, achieving a consistent improvement of 2.5-4.1 percentage points over BERT-based baselines on benchmark datasets.
Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not incorporate information on aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability to learn aspect representations from the large KGs in joint training with ALSC models. We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. A novel incorrect disambiguation detection technique addresses the problem of inaccuracy in aspect disambiguation. We also introduce the problem of determining mode significance in multi-modal explanation generation, and propose a two step solution. The proposed methods show a consistent improvement of 2.5 - 4.1 percentage points, over the recent BERT-based baselines on benchmark datasets. The code is available at https://github.com/mainuliitkgp/AR-BERT.git.