Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Methodology for Identifying Key Factors in Sentiment Analysis by Customer Characteristics Using Attention Mechanism

  • Lee, Kwangho (Graduate School of Business IT, Kookmin University) ;
  • Kim, Namgyu (School of Management Information Systems, Kookmin University)
  • Received : 2020.01.14
  • Accepted : 2020.03.16
  • Published : 2020.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, due to the increase of online reviews and the development of analysis technology, the interest and demand for online review analysis continues to increase. However, previous studies have not considered the emotions contained in each vocabulary may differ from one reviewer to another. Therefore, this study first classifies the customer group according to the customer's grade, and presents the result of analyzing the difference by performing review analysis for each customer group. We found that the price factor had a significant influence on the evaluation of products for customers with high ratings. On the contrary, in the case of low-grade customers, the degree of correspondence between the contents introduced in the mall and the actual product significantly influenced the evaluation of the product. We expect that the proposed methodology can be effectively used to establish differentiated marketing strategies by identifying factors that affect product evaluation by customer group.

최근 온라인 리뷰의 증가와 분석 기술의 발달로 인해 온라인 리뷰 분석에 대한 관심과 수요가 지속적으로 증가하고 있다. 하지만 리뷰 분석을 다룬 기존의 연구는 동일한 어휘라도 각 어휘에 담긴 감정은 리뷰어에 따라 서로 다를 수 있다는 점을 반영하지 못했다는 한계를 갖는다. 따라서 본 연구에서는 고객의 등급에 따라 고객군을 분류하고, 각 고객군별로 리뷰 분석을 수행하여 그 차이를 분석한 결과를 제시하였다. 의류 전문 쇼핑몰인 'M'사의 리뷰에 대한 분석을 수행한 결과, 쇼핑몰 사용도가 높은 고객의 경우 가격적인 요소가, 쇼핑몰 사용도가 낮은 고객의 경우 쇼핑몰에 소개된 내용과 실제 제품의 일치 정도가 제품의 긍/부정 평가에 크게 영향을 미치는 것으로 파악되었다. 제안 방법론은 고객군별로 제품 평가에 중요하게 영향을 미치는 요소를 식별함으로써, 차별화된 마케팅 전략을 수립하는 데에 효과적으로 활용될 수 있을 것으로 기대한다.

Keywords

References

  1. J. Wiebe, T. Wilson, R. Bruce, M. Bell, and M. Martin, "Learning Subjective Language," Computational linguistics, Vol. 30, No. 3, pp. 277-308, 2004. https://doi.org/10.1162/0891201041850885
  2. B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis," Foundations and Trends in Information Retrieval, 2008.
  3. B. Liu, "Sentiment Analysis: Mining Opinions, Sentiments, and Emotions," The Cambridge University Press, 2015.
  4. L. Zhang, S. Wang, and B. Liu, "Deep Learning for Sentiment Analysis: A Survey," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8, No. 4, e1253, 2018.
  5. H. Park, M. Song, and K. Shin, "Sentiment Analysis of Korean Reviews Using CNN: Focusing on Morpheme Embedding," Journal of intelligence and information systems, Vol. 24, No. 2, pp. 59-83, 2018. https://doi.org/10.13088/JIIS.2018.24.2.059
  6. P. Jeong and H. Ahn, "A Sentiment Analysis Model for Customer Reviews Considering Sentence Location," The Journal of Internet Electronic Commerce Research, Vol. 19, No. 1, pp. 167-186, 2019. https://doi.org/10.37272/JIECR.2019.02.19.1.167
  7. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality," Advances in Neural Information Processing systems, pp. 3111-3119, 2013.
  8. R. Socher, C. C. Lin, C. Manning, and A. Y. Ng, "Parsing Natural Scenes and Natural Language with Recursive Neural Networks," In Proceedings of the 28th international conference on machine learning(ICML-11), pp. 129-136, 2011.
  9. P. Chatterjee, "Online Reviews: Do Consumers Use Them?," Advance in Consumer Research, Vol. 28, pp. 129-133, 2001.
  10. F. Zhu and X. Zhang, "Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics," Journal of marketing, Vol. 74, No. 2, pp. 133-148, 2010. https://doi.org/10.1509/jm.74.2.133
  11. N. Korfiatis, E. G. Bariocanal, and S. S. Alonso, "Evaluating Content Quality and Helpfulness of Online Product Reviews: The interplay of review helpfulness vs. review content," Electronic Commerce Research and Applications, Vol. 11, No. 3, pp. 205-217, 2012. https://doi.org/10.1016/j.elerap.2011.10.003
  12. H. Lee and H. Park, "Investigation of Factors Affecting the Effects of Online Consumer Reviews," Informatization Policy, Vol. 20, No. 3, pp.3-17, 2013.
  13. P. Racherla and W. Friske, "Perceived 'Usefulness' of Online Consumer Reviews: An Exploratory Investigation Across Three Services Categories," Electronic Commerce Research and Applications, Vol. 11, No. 6, pp. 548-559, 2012. https://doi.org/10.1016/j.elerap.2012.06.003
  14. P. Briggs, B. BurFord, A. De Angeli, and P. Lynch, "Trust in Online Advice," Social Science Computer Review, Vol. 20, NO. 3, pp. 321-332, 2002. https://doi.org/10.1177/089443930202000309
  15. C. Forman, A. Ghose, and B. Wiesenfeld, "Examining the Relationship between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, Vol. 19, No. 3, pp. 291-313, 2008. https://doi.org/10.1287/isre.1080.0193
  16. A. Josang, R. Ismail, and C. Boyd, "A Survey of Trust and Reputation Systems for Online Service Provision," Decision support systems, Vol. 43, No. 2, pp. 618-644, 2007. https://doi.org/10.1016/j.dss.2005.05.019
  17. W. Medhat, A. Hassan, and H. Korashy, "Sentiment Analysis Algorithms and Applications: A Survey," Ain Shams engineering journal, Vol. 5, No. 4, pp. 1093-1113, 2014. https://doi.org/10.1016/j.asej.2014.04.011
  18. J. Song and S. Lee, "Automatic Construction of Positive/Negative Feature-Predicate Dictionary for Polarity Classification of Product Reviews," Journal of Computing Science and Engineering, Vol. 38, No. 3, pp. 115-177, 2011.
  19. E. You, Y. Kim, N. Kim, and S. Jeong, "Predicting the Direction of the Stock Index by Using a Domain-Specific Sentiment Dictionary," Journal of intelligence and information systems, Vol. 19 No. 1, pp. 95-110, 2013. https://doi.org/10.13088/jiis.2013.19.1.095
  20. S. Lee, J. Choi, and J. Kim, "Sentiment Analysis on Movie Review through Building Modified Sentiment Dictionary by Movie Genre," Journal of intelligence and information systems, Vol. 22, No. 2, pp. 97-113, 2016. https://doi.org/10.13088/jiis.2016.22.2.097
  21. Y. Kim, "Convolutional Neural Networks for Sentence Classification," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP), pp. 1746-1751, 2014.
  22. D. Bahdanau, K. Cho, and Y. Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," International Conference on Learning Representations 2015, 2014.
  23. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, "Hierarchical Attention Networks for Document Classification," In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp. 1480-1489, 2016.
  24. M. E. Peters, N. Mark, I.Mohit, G. Matt, C. Christopher, K. Lee, and Z. Luke, "Deep Contextualized Word Representations," arXiv:1802.05365, 2018.
  25. J. Devlin, M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," arXiv:1810.04805, 2018.
  26. Z. Yang, Z. Dai, Y. Yang, C. Jaime, R. S. Russ, and Q. V. Le, "XLNet: Generalized Autoregressive Pretraining for Language Understanding," arXiv:1906.08237, 2019.
  27. G. Nam and E. Jo, "Korean Text Sentiment Analysis," Communication-Books, 2017.