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@article{ART002982393},
author={Hyeon Gyu Kim},
title={Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={7},
pages={19-27},
doi={10.9708/jksci.2023.28.07.019}

TY - JOUR
AU - Hyeon Gyu Kim
TI - Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 7
PB - The Korean Society Of Computer And Information
SP - 19
EP - 27
SN - 1598-849X
AB - In the process of collecting social reviews, a number of noise reviews irrelevant to a given search keyword can be included in the search results. To filter out such reviews, machine learning can be used. However, if the number of reviews is insufficient for a target place to be analyzed, filtering accuracy can be degraded due to the lack of training data. To resolve this issue, we propose a supervised learning method to improve accuracy of the noise review filtering for the places with insufficient reviews. In the proposed method, training is not performed by an individual place, but by a group including several places with similar characteristics. The classifier obtained through the training can be used for the noise review filtering of an arbitrary place belonging to the group, so the problem of insufficient training data can be resolved. To verify the proposed method, a noise review filtering model was implemented using LSTM and BERT, and filtering accuracy was checked through experiments using real data collected online. The experimental results show that the accuracy of the proposed method was 92.4% on the average, and it provided 87.5% accuracy when targeting places with less than 100 reviews.
KW - Social review;Noise review filtering;Group learning;LSTM;BERT
DO - 10.9708/jksci.2023.28.07.019
ER -

Hyeon Gyu Kim. (2023). Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data. Journal of The Korea Society of Computer and Information, 28(7), 19-27.

Hyeon Gyu Kim. 2023, "Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data", Journal of The Korea Society of Computer and Information, vol.28, no.7 pp.19-27. Available from: doi:10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim "Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data" Journal of The Korea Society of Computer and Information 28.7 pp.19-27 (2023) : 19.

Hyeon Gyu Kim. Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data. 2023; 28(7), 19-27. Available from: doi:10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim. "Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data" Journal of The Korea Society of Computer and Information 28, no.7 (2023) : 19-27.doi: 10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim. Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data. Journal of The Korea Society of Computer and Information, 28(7), 19-27. doi: 10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim. Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data. Journal of The Korea Society of Computer and Information. 2023; 28(7) 19-27. doi: 10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim. Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data. 2023; 28(7), 19-27. Available from: doi:10.9708/jksci.2023.28.07.019

Hyeon Gyu Kim. "Improving Accuracy of Noise Review Filtering for Places with Insufficient Training Data" Journal of The Korea Society of Computer and Information 28, no.7 (2023) : 19-27.doi: 10.9708/jksci.2023.28.07.019