@inproceedings{chen-etal-2022-argument,
title = "Argument Mining for Review Helpfulness Prediction",
author = "Chen, Zaiqian and
Verdi do Amarante, Daniel and
Donaldson, Jenna and
Jo, Yohan and
Park, Joonsuk",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.609",
doi = "10.18653/v1/2022.emnlp-main.609",
pages = "8914--8922",
abstract = "The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features{---}such as the structure of arguments and the types of underlying elementary units{---}have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM$^2$) corpus{---}a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.",
}
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<abstract>The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features—such as the structure of arguments and the types of underlying elementary units—have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM²) corpus—a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.</abstract>
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%0 Conference Proceedings
%T Argument Mining for Review Helpfulness Prediction
%A Chen, Zaiqian
%A Verdi do Amarante, Daniel
%A Donaldson, Jenna
%A Jo, Yohan
%A Park, Joonsuk
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-argument
%X The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features—such as the structure of arguments and the types of underlying elementary units—have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM²) corpus—a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.
%R 10.18653/v1/2022.emnlp-main.609
%U https://aclanthology.org/2022.emnlp-main.609
%U https://doi.org/10.18653/v1/2022.emnlp-main.609
%P 8914-8922
Markdown (Informal)
[Argument Mining for Review Helpfulness Prediction](https://aclanthology.org/2022.emnlp-main.609) (Chen et al., EMNLP 2022)
ACL
- Zaiqian Chen, Daniel Verdi do Amarante, Jenna Donaldson, Yohan Jo, and Joonsuk Park. 2022. Argument Mining for Review Helpfulness Prediction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8914–8922, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.