글로벌 화장품 브랜드의 소비자 만족도 분석: 텍스트마이닝 기반의 사용자 후기 분석을 중심으로
Customer Satisfaction Analysis for Global Cosmetic Brands: Text-mining Based Online Review Analysis
0
조회
0
다운로드
13
인용
0
참고문헌
키워드
초록
Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.
Abstract
Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.
참고문헌 (21)
- Archak, N., "Deriving the pricing power of product features by mining consumer reviews", Management Science, 57(8), pp.1485~1509 (2011)
- Arora, S., "Identifying types of claims in online customer reviews. Association for Computational Linguistics", Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp.37~40 (2009)
- Bhandari, M., "What does the brand say? Effects of brand feedback to negative eWOM on brand trust and purchase intentions", International Journal of Advertising, 37(1), pp.125~141 (2018)
- Bhuta, S., "A review of techniques for sentiment analysis of Twitter data", 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, pp.583~591 (2014)
- Cambria, E., "New avenues in opinion mining and sentiment analysis", IEEE Intelligent Systems, 28(2), pp.15~21 (2013)
- Clemons, E., "When online reviews meet hyper differentiation : A study of the craft beer industry", Journal of Management Information Systems, 23(2), pp.149~171 (2006)
- Guellil, I., "Social big data mining: A survey focused on opinion mining and sentiments analysis", 2015 12th International Symposium on Programming and Systems, pp.1~10 (2015)
- Hennig-Thurau, T., "Electronic word-of-mouth via consumer-opinion platforms : What motivates consumers to articulate themselves on the Internet?", Journal of Interactive Marketing, 18(1), pp.1~38 (2004)
- Hu, N., "What do hotel customers complain about? Text analysis using", Tourism Management, 72, pp.417~426 (2019)
- 정나래, "SNS 특성에 의한 소비자의 화장품 구매태도가 관련정보와 구매 만족도에 미치는 영향", 아시안뷰티화장품학술지, 12(4), pp.565~573 (2014), 한국피부과학연구원
- Karamibekr, M., "A structure for opinion in social domains", 2013 International Conference on Social Computing(SocialCom) (2013)
- 이은재, "인터넷쇼핑 이용후기 메시지의 신뢰평가와구매시점 및 구매 후 행동특성연구", e-비즈니스연구, 8(3), pp.155~170 (2007), 국제e-비즈니스학회[DOI]
- Lee, T. Y., "Automated marketing research using online customer reviews", Journal of Marketing Research, 48(5), pp.881~894 (2011)
- Liao, S. H., "Mining product maps for new product development", Expert System with Application, 34(1), pp.50~62 (2008)
- Medhat, W., "Sentiment analysis algorithms and applications : A survey", Ain Shams Engineering Journal, 5(4), pp.1093~1113 (2014)
- 2016. Consumer Reports by Industry, "Media, D.", DMC, ~ (2016)
- Park, J, "Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands", IEEE Access, 8, pp.98526~98538 (2020)
- Ramadan, S., "Process mining of logged gaming behavior", 2019 International Conference on Process Minig(ICPM), pp.57~64 (2019)
- Xiang, Z., "What can big data and text analytics tell us about hotel guest experience and satisfaction?", International Journal of Hospitality Management, 44, pp.120~130 (2015)
- Zhan, J., "Gather customer concerns from online product reviews—A text summarization approach", Expert System with Applications, 36(2), pp.2107~2115 (2009)
- Zhan, Y., "Unlocking the power of big data in new product development", Annals of Operations Research, 270(1), pp.577~595 (2018)