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Ensemble Learning-Based Prediction of Good Sellers in Overseas Sales of Domestic Books and Keyword Analysis of Reviews of the Good Sellers

앙상블 학습 기반 국내 도서의 해외 판매 굿셀러 예측 및 굿셀러 리뷰 키워드 분석

  • 김도영 (동덕여자대학교 정보통계학과) ;
  • 김나연 (동덕여자대학교 정보통계학과) ;
  • 김현희 (동덕여자대학교 정보통계학과)
  • Received : 2022.08.02
  • Accepted : 2022.10.20
  • Published : 2023.04.30

Abstract

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

한국 문학이 세계적으로 관심을 받게 됨에 따라 해외 출판시장에서의 수요가 지속적으로 증가하고 있다. 따라서 해외 출판시 도서 판매량의 예측과 과거 해외 독자들의 선호도가 높았던 도서들의 특징을 분석하는 것이 중요하다. 본 논문에서는 최근 5년간 해외 출간된 도서 중에서 굿셀러로 분류되는 누적 5천 부 이상 판매 여부 예측 모델을 제안하고 굿셀러의 요인이 되는 변수들을 분석하였다. 이를 위해, XGBoost, Gradient Boosting, Adaboost, LightGBM, Random Forest의 다섯 개 앙상블 학습 모델과 Support Vector Machine, Logistic Regression, Deep Learning을 적용한 결과, 불균형 데이터 문제 해결에 앙상블 알고리즘이 큰 효과를 보였음을 확인했으며, 그 중에서도 LightGMB 모델이 99.86%의 AUC 값을 얻어 가장 좋은 예측 성능을 보임을 검증하였다. 예측을 위해 사용된 변수 중 가장 중요한 변수는 작가의 해외 출간 횟수로 나타났으며, 평점 평균, 상위 출판 시장 규모를 가진 국가에서 출판 여부와 평점 참여자 수 등이 중요한 변수로 나타났다. 또한, 굿셀러 도서에 대한 독자들의 반응을 분석하기 위해서, 굿셀러 도서 중에서도 가장 많이 판매된 4권의 작품 리뷰에 대해 텍스트 마이닝을 실시하였다. 분석 결과 스토리, 등장인물, 작가 순으로 관심을 둔 리뷰가 많았음을 알 수 있었으며, 평점이 낮은 리뷰로부터 번역 키워드가 도출된 것으로 보아, 번역에 대한 지원을 확대하는 것이 필요할 것으로 보인다.

Keywords

References

  1. M. Lee, "What is the best-selling Korean literature abroad? LTI Korea Research on the sales Korean literature published overseas in the last 5 years," Newspaper, 2022, http://www.news-paper.co.kr/news/articleView.html?idxno=76610
  2. Literature Translation Institute of Korea, [Internet] https://library.ltikorea.or.kr/
  3. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, Vol.16, No.1, pp.321-357, 2002. https://doi.org/10.1613/jair.953
  4. A. Geron, Hands-On Machine Learning with Skikit-Learn, Keras&TensorFlow, Orelly, 2019.
  5. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knoedge Discovery & Data Mining, pp.785-794, 2016.
  6. L. Breiman, "Arcing The Edge," Technical Report 486, Statistics Department, University of California at Berkeley, Jun. 1997.
  7. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, Vol.55, No.1, pp.119-139, 1997. https://doi.org/10.1006/jcss.1997.1504
  8. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T-Y Liu, "LightGBM: A highly efficient gradient boosting decision tree," in Proceedings of the 31st International Conference on Neural Information Processing Systems, pp.3149-3157, Dec. 2017.
  9. L. Breiman, "Random Forests," Machine Learning, Vol.45, pp.5-32, Jan. 2001. https://doi.org/10.1023/A:1010933404324
  10. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, "Support vector machines," IEEE Intelligent Systems and their Applications, Vol.13, No.4, pp.18-28, 1998. https://doi.org/10.1109/5254.708428
  11. D. R. Cox, "The Regression Analysis of Binary Sequences," Journal of the Royal Statistical Society, Series B, Vol.20, No.2, pp.215-242, 1958. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x
  12. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol.521, pp.436-444, 2015. https://doi.org/10.1038/nature14539
  13. B. Yucesoy, X. Wang, J. Huang, and A-L Barabasi, "Success in books: A big data approach to bestsellers," EPJ Data Science, Vol.7, 2018.
  14. S. K. Maity, A. Panigrahi, and A. Mukherjee, "Analyzing social book reading behavior on goodreads and how it predicts amazon best sellers," Influence and Behavior Analysis in Social Networks and Social Media, Sep. 2018.
  15. T. Q. Feng, M. Choy, and M. N. Laik, "Predicting book sales trend using deep learning framework," International Journal of Advanced Computer Science and Applications, Vol.11, No.2, pp.28-39, 2020. https://doi.org/10.14569/IJACSA.2020.0110205
  16. Amazon [Internet], https://www.amazon.com/
  17. Goodreads [Internet], https://www.goodreads.com/
  18. R. Lawsonl, Web Scraping with Python, Packt Publishing, 2015.
  19. C. Hutto and E. Gilbert, "VADER: A parsimonious rulebased model for sentiment analysis of social media text," in Proceedings of International Conference on Weblogs and Social Media, Vol.8, pp.216-225, Jan. 2015.
  20. J. Devlin, M-W Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," in Proceedings of North American Chapter of the Association for Computational Linguistics, pp.4171-4186, 2019.
  21. N. V. Chawla, A. Lazarevic, and O. Hall, "Smoteboost: Improving prediction of the minority class in boosting," in Proceedings of Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.107-119, 2003.
  22. C. Seiffert, T. M. Khoshgoftaar, J. Hulse, and A. Napolitano, "RUSBoost: A hybrid approach to alleviating class imbalance," Institute of Electrical and Electronics Engineers, pp.185-197, 2010.