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Review score prediction requires review text understanding, a criticalreal-world application of natural language processing. Due to dissimilar textdomains in product reviews, a common practice is fine-tuning BERT models uponreviews of differing domains. However, there has not yet been an empiricalstudy of cross-domain behaviors of BERT models in the various tasks of productreview understanding. In this project, we investigate text classification BERTmodels fine-tuned on single-domain and multi-domain Amazon review data. In ourfindings, though single-domain models achieved marginally improved performanceon their corresponding domain compared to multi-domain models, multi-domainmodels outperformed single-domain models when evaluated on multi-domain data,single-domain data the single-domain model was not fine-tuned on, and onaverage when considering all tests. Though slight increases in accuracy can beachieved through single-domain model fine-tuning, computational resources andcosts can be reduced by utilizing multi-domain models that perform well acrossdomains.

Investigating Cross-Domain Behaviors of BERT in Review Understanding