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http://dx.doi.org/10.15207/JKCS.2020.11.11.009

A Study on SNS Reviews Analysis based on Deep Learning for User Tendency  

Park, Woo-Jin (Department of Computer Engineering, Sejong University)
Lee, Ju-Oh (Department of Computer Engineering, Sejong University)
Lee, Hyung-Geol (Department of Computer Engineering, Sejong University)
Kim, Ah-Yeon (Department of Computer Engineering, Sejong University)
Heo, Seung-Yeon (Department of Computer Engineering, Sejong University)
Ahn, Yong-Hak (Department of Computer Engineering, Sejong University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.11, 2020 , pp. 9-17 More about this Journal
Abstract
In this paper, we proposed an SNS review analysis method based on deep learning for user tendency. The existing SNS review analysis method has a problem that does not reflect a variety of opinions on various interests because most are processed based on the highest weight. To solve this problem, the proposed method is to extract the user's personal tendency from the SNS review for food. It performs classification using the YOLOv3 model, and after performing a sentiment analysis through the BiLSTM model, it extracts various personal tendencies through a set algorithm. Experiments showed that the performance of Top-1 accuracy 88.61% and Top-5 90.13% for the YOLOv3 model, and 90.99% accuracy for the BiLSTM model. Also, it was shown that diversity of the individual tendencies in the SNS review classification through the heat map. In the future, it is expected to extract personal tendencies from various fields and be used for customized service or marketing.
Keywords
SNS; Deep Learning; Opiniom Mining; Object Detection; YOLOv3; BiLSTM;
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Times Cited By KSCI : 3  (Citation Analysis)
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