@inproceedings{lee-etal-2023-painsight,
title = "Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews",
author = "Lee, Yukyung and
Kim, Jaehee and
Kim, Doyoon and
Kho, Yookyung and
Kim, Younsun and
Kang, Pilsung",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.20",
doi = "10.18653/v1/2023.wassa-1.20",
pages = "215--227",
abstract = "As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.",
}
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<abstract>As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.</abstract>
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%0 Conference Proceedings
%T Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
%A Lee, Yukyung
%A Kim, Jaehee
%A Kim, Doyoon
%A Kho, Yookyung
%A Kim, Younsun
%A Kang, Pilsung
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lee-etal-2023-painsight
%X As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.
%R 10.18653/v1/2023.wassa-1.20
%U https://aclanthology.org/2023.wassa-1.20
%U https://doi.org/10.18653/v1/2023.wassa-1.20
%P 215-227
Markdown (Informal)
[Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews](https://aclanthology.org/2023.wassa-1.20) (Lee et al., WASSA 2023)
ACL
- Yukyung Lee, Jaehee Kim, Doyoon Kim, Yookyung Kho, Younsun Kim, and Pilsung Kang. 2023. Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 215–227, Toronto, Canada. Association for Computational Linguistics.