Earticle

초록

영어

In the dynamic world of e-commerce, effectively deciphering customer reviews is of paramount importance. This study uniquely combines the RFE-SHAP feature selection technique with topic modeling (LDA) to address prevalent challenges like overfitting in predictive modeling. Our empirical analysis underscores the superior performance of the Random Forest model, particularly when refined with a subset of 14 pivotal features. Notably, topics such as quality and appearance, fit and comfort, and durability concerns emerged as significant determinants of customer satisfaction within the clothing sector. Utilizing data exclusively from Amazon's clothing reviews, our research emphasizes the criticality of strategic feature selection and delves deep into the multifaceted factors shaping customer sentiments. By seamlessly merging quantitative metrics with qualitative content insights, this study not only offers a robust framework for understanding online reviews but also paves the way for future research in optimizing e-commerce strategies based on customer feedback.

목차

Abstract
1. Introduction
2. Literature review
2.1. Customer Satisfaction and Sentiment Analysis
2.2. Content Analysis of Reviews: LDA
2.3. RFE-SHAP and eXplanation of content differences
3. Research Framework and Analysis
4. Results
5. Conclusion and Discussion
Acknowledgments
References

키워드

저자

• Olga Chernyaeva [ Pusan National University, College of Business Administration ]
• Taeho Hong [ Pusan National University, College of Business Administration ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658