Acknowledgement
이 논문은 2022년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2022S1A5B5A16055001)
References
- 김성수, 배준호, 이주현, 정희주, 김희웅. (2023). TeGCN: 씬파일러 신용평가를 위한 트랜스포머 임베딩 기반 그래프 신경망 구조 개발. 지능정보연구, 29(3), 419-437. https://doi.org/10.13088/JIIS.2023.29.3.419
- 류동엽, 이흠철, 김재경. (2023). XAI 기법을 이용한 리뷰 유용성 예측 결과 설명에 관한 연구. 지능정보연구, 29(2), 35-56. https://doi.org/10.13088/JIIS.2023.29.2.035
- 현우창, 이인수, 서봉원. (2023). 그래프 신경망을 활용한 온라인 의견 사기 탐지. 정보과학회논문지, 50(11), 985-994.
- 홍태호, 원종관, 김은미, 김민수. (2023). 설명 가능한 인공지능과 CNN을 활용한 암호화폐 가격 등락 예측모형. 지능정보연구, 29(2), 129-148. https://doi.org/10.13088/JIIS.2023.29.2.129
- Adak, A., Pradhan, B., & Shukla, N. (2022). Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: Systematic review. Foods, 11(10), 1500.
- Azzone, M., Barucci, E., Moncayo, G. G., & Marazzina, D. (2022). A machine learning model for lapse prediction in life insurance contracts. Expert Systems with Applications, 191, 116261.
- Cao, Q., Duan, W., & Gan, Q. (2011). Explorng determinants of voting for the "helpfulness" of online user reviews: A text mining approach. Decision Support Systems, 50, 511-521. https://doi.org/10.1016/j.dss.2010.11.009
- Coussement, K., & Benoit, D. F. (2021). Interpretable data science for decision making. Decision Support Systems, 150, 113664.
- Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421.
- Du, J., Rong, J., Wang, H., & Zhang, Y. (2021). Neighbor-aware review helpfulness prediction. Decision Support Systems, 148, 113581.
- Eslami, S. P., Ghasemaghaei, M., & Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32-42. https://doi.org/10.1016/j.dss.2018.06.012
- Fan, M., Feng, C., Guo, L., Sun, M., & Li, P. (2019, May). Product-aware helpfulness prediction of online reviews. In The world wide web conference (pp. 2715-2721).
- Fresneda, J. E., & Gefen, D. (2019). A semantic measure of online review helpfulness and the importance of message entropy. Decision Support Systems, 125, 113117.
- Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE transactions on knowledge and data engineering, 23(10), 1498-1512. https://doi.org/10.1109/TKDE.2010.188
- Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
- Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161-168. https://doi.org/10.1016/j.jretconser.2018.02.006
- Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert systems with applications, 117, 287-299. https://doi.org/10.1016/j.eswa.2018.09.039
- Jain, D. K., Rahate, A., Joshi, G., Walambe, R., & Kotecha, K. (2022). Employing Co-Learning to Evaluate the Explainability of Multimodal Sentiment Analysis. IEEE Transactions on Computational Social Systems.
- Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207, 117921.
- Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
- Krishnamoorthy, S. (2015). Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42(7), 3751-3759. https://doi.org/10.1016/j.eswa.2014.12.044
- Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845.
- Liu, J., Chen, Y., Huang, X., Li, J., & Min, G. (2023). GNN-based long and short term preference modeling for next-location prediction. Information Sciences, 629, 1-14.
- Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism management, 47, 140-151. https://doi.org/10.1016/j.tourman.2014.09.020
- Lundberg, S. M., Erion, G. G., & Lee, S. I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888.
- Mohammad, S. M. (2017). Word affect intensities. arXiv preprint arXiv:1704.08798.
- Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 34(1), 185-200. https://doi.org/10.2307/20721420
- Olmedilla, M., Martinez-Torres, M. R., & Toral, S. (2022). Prediction and modelling online reviews helpfulness using 1D Convolutional Neural Networks. Expert Systems with Applications, 198, 116787.
- Plutchik, R. (1980). Emotion: A Psychoevolutionary Synthesis. NY Harper and Row.
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). "Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.
- Saptono, R., & Mine, T. (2022, October). Best Approximate Distribution-based Model for Helpful Vote of Customer Review Prediction. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3427-3434.
- Saumya, S., Roy, P. K., & Singh, J. P. (2023). Review helpfulness prediction on e-commerce websites: A comprehensive survey. Engineering Applications of Artificial Intelligence, 126, 107075.
- Saumya, S., Singh, J. P., Baabdullah, A. M., Rana, N. P., & Dwivedi, Y. K. (2018). Ranking online consumer reviews. Electronic commerce research and applications, 29, 78-89. https://doi.org/10.1016/j.elerap.2018.03.008
- Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
- Shi, Z., Lee, G. M., & Whinston, A. B. (2016). Toward a Better Measure of Business Proximity. MIS quarterly, 40(4), 1035-1056. https://doi.org/10.25300/MISQ/2016/40.4.11
- Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1-12. https://doi.org/10.1016/j.dss.2018.01.004
- Wang, X., Tang, L. R., & Kim, E. (2019). More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness?. International Journal of Hospitality Management, 77, 438-447.
- Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, 32.
- Zheng, T., Lin, Z., Zhang, Y., Jiao, Q., Su, T., Tan, H., ... & Law, R. (2023). Revisiting review helpfulness prediction: An advanced deep learning model with multimodal input from Yelp. International Journal of Hospitality Management, 114, 103579.
- Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81.