Improving Document-Level Sentiment Analysis with User and Product Context

Chenyang Lyu, Jennifer Foster, Yvette Graham


Abstract
Past work that improves document-level sentiment analysis by encoding user and product in- formation 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 his- torical 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.
Anthology ID:
2020.coling-main.590
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6724–6729
Language:
URL:
https://aclanthology.org/2020.coling-main.590
DOI:
10.18653/v1/2020.coling-main.590
Bibkey:
Cite (ACL):
Chenyang Lyu, Jennifer Foster, and Yvette Graham. 2020. Improving Document-Level Sentiment Analysis with User and Product Context. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6724–6729, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Improving Document-Level Sentiment Analysis with User and Product Context (Lyu et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.590.pdf
Code
 lyuchenyang/Document-level-Sentiment-Analysis-with-User-and-Product-Context