Improvement of recommendation system using attribute-based opinion mining of online customer reviews (온라인 고객리뷰에 대한 속성기반 오피니언 마이닝을 활용한 추천시스템의 개선)

Misun Lee (이미선); Ahn, Hyunchul (안현철)

doi:10.9708/jksci.2023.28.12.259

Improvement of recommendation system using attribute-based opinion mining of online customer reviews

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(12), pp.259-266
DOI : 10.9708/jksci.2023.28.12.259
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 7, 2023
Accepted : November 28, 2023
Published : December 29, 2023

Misun Lee ¹, Ahn, Hyunchul ²

¹세종대학교
²국민대학교

Accredited

ABSTRACT

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

KEYWORDS

Recommender System, Collaborative Filtering, Aspect-based Opinion Mining, App Store, Online Consumer Review

Citation status

* References for papers published after 2023 are currently being built.

[journal] 이륜경 / 2019 / Developing the online reviews based recommender models for multi-attributes using deep learning / 정보시스템연구 / 한국정보시스템학회 28(1) : 97~114

[journal] 최준연 / 2013 / Applying Rating Score’s Reliability of Customers to Enhance Prediction Accuracy in Recommender System / 한국콘텐츠학회 논문지 / 한국콘텐츠학회 13(7) : 379~385

[journal] 장경애 / 2015 / Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign / 정보과학회논문지 / 한국정보과학회 42(4) : 512~521

[journal] Ewa Maslowska / 2017 / Do customer reviews drive purchase decisions? The moderating roles of review exposure and price / Decision Support Systems / Elsevier BV 98 : 1~9

[journal] 이종화 / 2015 / A Study on Customer Reviews about Domestic and Imported Clothes Products through Opinion Mining / 인터넷전자상거래연구 / 한국인터넷전자상거래학회 15(3) : 223~234

[confproc] Herlocker, J. L. / 1999 / An algorithmic framework for performing collaborative filtering / Proc. of the 22nd International ACMSIGIR Research and Development Conference on Information Retrieval : 230~237

[journal] 최슬비 / 2016 / Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users / 지능정보연구 / 한국지능정보시스템학회 22(3) : 113~127

[journal] Kim Schouten / 2015 / Survey on Aspect-Level Sentiment Analysis / IEEE Transactions on Knowledge and Data Engineering / Institute of Electrical and Electronics Engineers (IEEE) 28(3) : 813~830

[journal] D. M. Blei / 2003 / Latent Dirichlet Allocation / Journal of Machine Learning Research 3 : 993~1022

[confproc] C. Chiru / 2014 / Comparison between LSA-LDA-Lexical Chains / Proceedings of the 10th International Conference on Web Information Systems and Technologies 2 : 255~262

[confproc] M. Hu / 2004 / Mining and Summarizing Customer Reviews / Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04) : 168~177

[journal] 전병국 / 2015 / A Collaborative Filtering System Combined with Users’ Review Mining: Application to the Recommendation of Smartphone Apps / 지능정보연구 / 한국지능정보시스템학회 21(2) : 1~18

[book] G. Shani / 2011 / Recommender Systems Handbook / Springer : 257~297

[journal] T. Chai / 2014 / Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature / Geoscientific Model Development / Copernicus GmbH 7(3) : 1247~1250

[confproc] P. Cremonesi / 2010 / Performance of Recommender Algorithms on Top-N Recommendation / Proceedings of the 4th ACM Conference on Recommender Systems : 39~46

[journal] Jonathan L. Herlocker / 2004 / Evaluating collaborative filtering recommender systems / ACM Transactions on Information Systems / Association for Computing Machinery (ACM) 22(1) : 5~53

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improvement of recommendation system using attribute-based opinion mining of online customer reviews

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improvement of recommendation system using attribute-based opinion mining of online customer reviews

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.