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Improving document-level sentiment analysis with user and product context

Lyu, Chenyang, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and Graham, Yvette (2020) Improving document-level sentiment analysis with user and product context. In: Proceedings of the 28th International Conference on Computational Linguistics, 8-13 Dec 20, Barcelona, Spain (Online).

Past work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of historical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 28th International Conference on Computational Linguistics. . International Committee on Computational Linguistics (ICCL).
Publisher:International Committee on Computational Linguistics (ICCL)
Official URL:https://dx.doi.org/10.18653/v1/2020.coling-main.59...
Copyright Information:© 2020 The Authors. (CC-BY-4.0)
Funders:Science Foundation Ireland (SFI) Centre for Research Training in Machine Learning (18/CRT/6183)
ID Code:25959
Deposited On:03 Jun 2021 10:45 by Jennifer Foster . Last Modified 03 Jun 2021 10:47

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