Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer
This work addresses CPC for applications like comparative question answering and review-based recommendations, but it is incremental as it builds on prior graph-based approaches by adding sentiment analysis.
The paper tackles Comparative Preference Classification (CPC) by predicting preference comparisons between entities in sentences, and it introduces SAECON, which improves accuracy using sentiment analysis via domain adaptive knowledge transfer, achieving significant F1 score gains over existing methods.
We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendations. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We proposed sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.