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        <title>Improving Document-Level Sentiment Analysis with User and Product Context</title>
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    <name type="personal">
        <namePart type="given">Chenyang</namePart>
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        <namePart type="given">Jennifer</namePart>
        <namePart type="family">Foster</namePart>
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    <name type="personal">
        <namePart type="given">Yvette</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.590</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Improving Document-Level Sentiment Analysis with User and Product Context
%A Lyu, Chenyang
%A Foster, Jennifer
%A Graham, Yvette
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lyu-etal-2020-improving
%X 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.
%R 10.18653/v1/2020.coling-main.590
%U https://aclanthology.org/2020.coling-main.590
%U https://doi.org/10.18653/v1/2020.coling-main.590
%P 6724-6729

Markdown (Informal)

[Improving Document-Level Sentiment Analysis with User and Product Context](https://aclanthology.org/2020.coling-main.590) (Lyu et al., COLING 2020)

• Improving Document-Level Sentiment Analysis with User and Product Context (Lyu et al., COLING 2020)

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.