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); Hyunchul Ahn (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, Hyunchul Ahn 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.

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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.