Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis
This work improves sentiment analysis accuracy for review platforms by better leveraging contextual data, though it appears incremental as it builds on existing BERT-based architectures.
The paper tackles sentiment analysis by incorporating historical reviews and user-product textual associations, achieving substantial improvements over previous state-of-the-art methods on IMDb, Yelp-2013, and Yelp-2014 benchmarks.
User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art. Since we employ BERT-base as the encoder, we additionally provide experiments in which our approach performs well with Span-BERT and Longformer. Furthermore, experiments where the reviews of each user/product in the training data are downsampled demonstrate the effectiveness of our approach under a low-resource setting.