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http://dx.doi.org/10.9716/KITS.2022.21.4.075

A Methodology for Predicting Changes in Product Evaluation Based on Customer Experience Using Deep Learning  

An, Jiyea (국민대학교 비즈니스IT전문대학원)
Kim, Namgyu (국민대학교 비즈니스IT전문대학원)
Publication Information
Journal of Information Technology Services / v.21, no.4, 2022 , pp. 75-90 More about this Journal
Abstract
From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.
Keywords
Deep learning; BERT; Review Analysis; Future Prediction; Text Classification; Customer Experience;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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1 Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, "Attention Is All You Need", Proc. of the 31st International Conference on Neural Information Processing Systems (NIPS), 2017, 6000-6010.
2 Mikolov, T., M. Karafiat, L. Burget, and J. Cernocky, "Recurrent neural network based lan- guage model", Eleventh Annual Conference of the International Speech Communication Asso ciation, Vol.2, No.3, 2010, 1045-1048.
3 정상빈, 김광용, 임은택, 김도연, "소비자 리뷰 특성이 모바일 커머스 성과에 미치는 영향에 관한 연구", 글로벌경영학회지, 2020, 219-234.
4 Chung, J., C. Gulcehre, K. Cho, and Y. Bengio, "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling", arXiv preprint arXiv:1412.3555, 2014.
5 김선주, 박대영, 김병수, "토픽모델링을 이용한 Airbnb 고객의 리뷰 분석: 코로나 시대 전과 후의 토픽 차이를 중심으로", 인터넷전자상거래연구, 제21권, 제4호, 2021, 115-130.
6 엄기홍, 김대식, "온라인 정치 여론 분석을 위한 댓글 분류기의 개발과 적용: KoBERT를 활용한 여론분석", 한국정당학회보, 제20권, 제3호, 2021, 167-191.
7 조희련, 이유미, 임현열, 차준우, 이찬규, "딥러닝 기반언어모델을 이용한 한국어 학습자 쓰기 평가의 자동 점수 구간 분류: KoBERT와 KoGPT2를 중심으로", 한국언어문화학, 제18권, 제1호, 2021, 217-241.
8 Devlin, J., M.W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding", arXiv preprint arXiv: 1810.04805, 2018.
9 Hochreiter, S. and J. Schmidhuber, "Long ShortTerm Memory", Journal of Neural Computation, Vol.9, No.8, 1997 ,1735-1780.   DOI
10 Lee, K. and N. Kim, "Methodology for Identifying Key Factors in Sentiment Analysis by Customer Characteristics Using Attention Mechanism", Journal of The Korea Society of Computer and Information, Vol.25, No.3, 2020, 207-218.   DOI
11 Kim, H., "A Study on Brand Image Analysis of Gaming Business Corporation using KoBERT and Twitter Data", Journal of Korea Game Society, Vol.21, No.6, 2021, 75-85.   DOI
12 Fang, X. and J. Zhan, "Sentiment analysis using product review data", Journal of Big Data, Vol.2, No.1, 2015, 1-14.   DOI
13 Gupta, V. and G.S. Lehal, "A survey of text mining techniques and applications", Journal of Emerging Technologies in Web Intelligence, Vol.1, No.1, 2009, 60-76.
14 Hu, Z., J. Hu, W. Ding, and X. Zheng, Review sentiment analyis based on deep learning", International Conference on e-Business Engineering (IEEE), Vol.12, 2015, 87-94.
15 Li, S.S. and E. Karahanna, "Online recommendation systems in a B2C E-commerce context: A review and future directions", Journal of the Association for Information Systems, Vol.16, No.2, 2015, 2.
16 Mikolov, T., K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space", arXiv preprint arXiv:1301.3781, 2013.
17 Vanaja, S. and M. Belwal, "Aspect-level sentiment analysis on e-commerce data", International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 2018, 1275-1279.
18 Radford, A., K. Narasimhan, T. Salimans, and I. Sutskever, "Improving language understanding by generative pre-training", 2018, Available at https://s3-us-west-2.amazonaws.com/openaiassets/research-covers/languageunsupervised/languageunderstandingpaper.pdf(Downloaded March 16, 2022).
19 Sulthana, A.N. and S. Vasantha, "Influence of electronic word of mouth eWOM on purchase intention", International Journal of Scientific & Technology Research, Vol.8, No.10, 2019, 1-5.
20 Umer, M., I. Ashraf, A. Mehmood, S. Ullah, and G.S. Choi, "Predicting numeric ratings for google apps using text features and ensemble learning", ETRI Journal, Vol.43, No.1, 2021, 95-108.   DOI
21 Yessenov, K. and S. Misailovic, "Sentiment analysis of movie review comments", Methodology, Vol.17, 2009, 1-7.
22 Zhang, L., S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.8, No.4, 2018, 201
23 Sutskever, I., O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks", In Proceedings. of the 27th International Conference on Neural Information Processing Systems(NIPS), Vol.2, 2014, 3104-3112.
24 Qu, X., X. Li, and J.R. Rose, "Review helpfulness assessment based on convolutional neural network", arXiv preprint arXiv:1808.09016, 2018.
25 Roberts, M.E., B.M. Stewart, and D. Tingley, "Stm: An R package for structural topic models", Journal of Statistical Software, Vol.91, 2019, 1-40.
26 SKT Open Source, "SK telecom Open Source Projects: KoBERT", Available at https://sktelecom.github.io/project/kobert/(Accessed March 16, 2022)
27 전병국, 안현철, "사용자 리뷰 마이닝을 활용한 협업 필터링 앱 추천 시스템", 한국지능정보시스템학회 학술대회논문집, 2015, 25-44.
28 김광국, 김용환, 김자희, "사용자 리뷰 토픽분석을 활용한 모바일 쇼핑 앱 고객만족도에 관한 연구", 한국전자거래학회지, 제23권, 제4호, 2018, 41-62.   DOI
29 노민정, "리뷰의 품질이 리뷰의 유용성에 미치는 영향: 리뷰 별점의 조절효과를 중심으로", 한국디지털콘텐츠학회논문지, 제22권, 제6호, 2021, 999-1007.
30 박정현, 이서호, 임규진, 여운영, "마켓 인사이트를 위한 상품 리뷰의 다차원 분석 방안", 지능정보연구, 제26권, 제2호, 2020, 57-78.   DOI
31 정지훈, 정혜인, 이준기, "텍스트마이닝 기법과 ARIMA 모형을 활용한 배달의 민족 앱 리뷰 분석", 한국디지털콘텐츠학회논문지, 제22권, 제2호, 2021, 291-299.
32 조민경, 이병주, "토픽모델링을 통한 국내 대형항공사들의 서비스 품질 비교: 트립어드바이저 리뷰를 중심으로", 호텔관광연구, 제23권, 제1호, 2021, 152-165.
33 최준영, 임희석, "자연어처리 모델을 이용한 이커머스 데이터 기반 감성 분석 모델 구축", 한국융합학회논문지, 제11권, 제11호, 2020, 33-39.   DOI
34 Ain, Q.T., M. Ali, A. Riaz, A. Noureen, M. Kamran, B. Hayat, and A. Rehman, "Sentiment analysis using deep learning techniques: A review", Int. J. Adv. Comput. Sci. Appl., Vol.8, No.6, 2017, 424.
35 Bahdanau, D., K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate", arXiv preprint arXiv: 1409.0473, 2014.
36 Bustos, O. and A. Pomares-Quimbaya, "Stock market movement forecast: A systematic review", Expert Systems with Applications, 2020, 156:113464.   DOI
37 Park, H. and K. Kim, "Recommender system using BERT sentiment analysis", Journal of Intelligence and Information Systems, Vol.27, No.1, 2021, 1-15.   DOI
38 Bhatt, A., A. Patel, H. Chheda, and K. Gawande, "Amazon review classification and sentiment analysis", International Journal of Computer Science and Information Technologies, Vol.6, No.6, 2015, 5107-5110.
39 Blei, D. M., A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation", Journal of machine Learning research, Vol.3, 2003, 993-1022.
40 Bojanowski, P., E. Grave, A. Joulin, and T. Mikolov, "Enriching Word Vectors with Subword Information", Transactions of the Association for Computational Linguistics, Vol.5, 2017, 135-146.   DOI
41 현지연, 유상이, 이상용, "평점과 리뷰 텍스트 감성분석을 결합한 추천시스템 향상 방안 연구", 지능정보연구, 제25권, 제1호, 2019, 219-239.   DOI
42 장예화, 이청용, 최일영, 김재경, "리뷰 데이터 마이닝을 이용한 하이브리드 추천시스템 개발: Amazon Kindle Store 데이터 분석사례", Information Systems Review, 제23권, 제1호, 2021, 155-172.   DOI