Earticle

초록

영어

This study demonstrates how online hotel reviews can classify customer loyalty by analyzing textual features such as sentiment, rating, and loyalty keywords. Using a review dataset from TripAdvisor, the research applies sentiment analysis tools (VADER, TextBlob, SenticNet) and topic modeling techniques to classify loyalty. Results show that loyalty keywords imply a significant difference in their presence between loyal and non-loyal customers. At the same time, a considerable difference in their presence between loyal and non-loyal customers, while sentiment scores present significantly moderate results. However, Rao-Striling diversity and review length do not exhibit a significant difference. The study contributes a framework for using review content to identify loyal customers, offering practical methods for businesses to enhance customer retention and improve CRM strategies. Limitations include reliance on sentiment scores for loyalty classification and the exclusion of behavioral data, suggesting future research could incorporate more comprehensive customer data for validation.

목차

Abstract
1. Introduction
2. Literature Review
2.1 The Impact of Online Reviews on Consumer Loyalty
2.2 Lexicon-Based Sentiment Analysis Approaches in Consumer Research
2.3 Online review characteristics
3. Methodology and Research Framework Analysis
4. Results
5. Discussion
Acknowledgments
References

키워드

저자

• Altynsara Baktiyar [ 부산대학교 경영학과 ]
• Eunmi Kim [ 부산대학교 경영연구원 ]
• Taeho Hong [ 부산대학교 경영학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658