Identification of Opinion and Ground in Customer Review Using Heterogeneous Datasets

Po-Min Chuang, Kiyoaki Shirai, Natthawut Kertkeidkachorn

2024

Abstract

Online reviews are a valuable source of information for both potential buyers and enterprises, but not all reviews provide us helpful information. This paper aims at the identification of a user’s opinion and its reason or ground in a review, supposing that a review including a ground for an opinion is helpful. A classifier to identify an opinion and a ground, called the opinion-ground classifier, is trained from three heterogeneous datasets. The first is the existing dataset for discourse analysis, KWDLC, which is the manually labeled but out-domain dataset. The second is the in-domain but weakly supervised dataset made by a rule-based method that checks the existence of causality discourse markers. The third is another in-domain dataset augmented by ChatGPT, where a prompt to generate new samples is given to ChatGPT. We train several models as the opinion-ground classifier. Results of our experiments show that the use of automatically constructed datasets significantly improves the classification performance. The F1-score of our best model is 0 .71, which is 0.12 points higher than the model trained from the existing dataset only.

Paper Citation

in Harvard Style

Chuang P., Shirai K. and Kertkeidkachorn N. (2024). Identification of Opinion and Ground in Customer Review Using Heterogeneous Datasets. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 68-78. DOI: 10.5220/0012307000003636

in Bibtex Style

@conference{icaart24,
author={Po-Min Chuang and Kiyoaki Shirai and Natthawut Kertkeidkachorn},
title={Identification of Opinion and Ground in Customer Review Using Heterogeneous Datasets},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={68-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012307000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Identification of Opinion and Ground in Customer Review Using Heterogeneous Datasets
SN - 978-989-758-680-4
AU - Chuang P.
AU - Shirai K.
AU - Kertkeidkachorn N.
PY - 2024
SP - 68
EP - 78
DO - 10.5220/0012307000003636
PB - SciTePress