@inproceedings{sun-etal-2020-semi,
title = "Semi-supervised Category-specific Review Tagging on {I}ndonesian {E}-Commerce Product Reviews",
author = "Sun, Meng and
Leo, Marie Stephen and
Munawwar, Eram and
Condylis, Paul C. and
Kong, Sheng-yi and
Lee, Seong Per and
Hidayat, Albert and
Kerianto, Muhamad Danang",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 3rd Workshop on e-Commerce and NLP",
month = jul,
year = "2020",
address = "Seattle, WA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecnlp-1.9",
doi = "10.18653/v1/2020.ecnlp-1.9",
pages = "59--63",
abstract = "Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either {``}category-specific{''} topics or as {``}generic{''} topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.",
}
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<abstract>Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either “category-specific” topics or as “generic” topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews
%A Sun, Meng
%A Leo, Marie Stephen
%A Munawwar, Eram
%A Condylis, Paul C.
%A Kong, Sheng-yi
%A Lee, Seong Per
%A Hidayat, Albert
%A Kerianto, Muhamad Danang
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 3rd Workshop on e-Commerce and NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA
%F sun-etal-2020-semi
%X Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either “category-specific” topics or as “generic” topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.
%R 10.18653/v1/2020.ecnlp-1.9
%U https://aclanthology.org/2020.ecnlp-1.9
%U https://doi.org/10.18653/v1/2020.ecnlp-1.9
%P 59-63
Markdown (Informal)
[Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews](https://aclanthology.org/2020.ecnlp-1.9) (Sun et al., ECNLP 2020)
ACL
- Meng Sun, Marie Stephen Leo, Eram Munawwar, Paul C. Condylis, Sheng-yi Kong, Seong Per Lee, Albert Hidayat, and Muhamad Danang Kerianto. 2020. Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews. In Proceedings of the 3rd Workshop on e-Commerce and NLP, pages 59–63, Seattle, WA, USA. Association for Computational Linguistics.