- Apply for Authority
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS
ISSN : 1738-3110
Topics and Sentiment Analysis Based on Reviews of Omni-Channel Retailing
YOO, Byong-Kook
- Downloaded
- Viewed
Abstract
Purpose: This study aims to analyze the factors affecting customer satisfaction in the customer reviews of omni-channel, posted on Internet blogs, cafes, and YouTube using text mining analysis. Research, data, and Methodology: In this study, frequency analysis is performed and the LDA (Latent Dirichlet Allocation) is used to analyze social big data to respond to reviewers' reaction to the recently opened omni-channel shopping reviews by L Shopping Company. Additionally, based on the topic analysis, we conduct a sentiment analysis on purchase reviews and analyze the characteristics of each topic on the positive or negative sentiments of omni-channel app users. Results: As a result of a topic analysis, four main topics are derived: delivery and events, economic value, recommendations and convenience, and product quality and brand awareness. The emotional analysis reveals that the reviewers have many positive evaluations for price policy and product promotion, but negative evaluations for app use, delivery, and product quality. Conclusions: Retailers can establish customized marketing strategies by identifying the customer's major interests through text mining analysis. Additionally, the analysis of sentiment by subject becomes an important indicator for developing products and services that customers want by identifying areas that satisfy customers and areas that evoke negative reactions.
- keywords
- Omni-channel, Customer Satisfaction, TOPIC, LDA (Latent Dirichlet Allocation), Emotion Analysis
Reference
Aaker, D. A. (1991). Managing Brand Equity. The Free Press, New York.
Anderson, R. E., & Srinivasan, S. S. (2003). E‐ satisfaction and E‐ loyalty: A Contingency Framework. Psychology &marketing, 20(2), 123-138.
Avery, J., Steenburgh, J., Deighton, T. J., & Caravella, M. (2012). Adding Bricks to Clicks: Predicting the Patterns of Crosschannel Elasticities Over Time. Journal of Marketing, 76(3), 96-111.
Bell, D. R., Gallino, S., & Moreno, A. (2014). How to Win in an Omni channel World. MIT Sloan Management Review, 56(1), 45-53.
Blei, D. M., Ng, A.Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Bhalla, R. (2014). The Omni-channel Customer Experience:Driving Engagement Through Digitisation. Journal of Digital & Social Media Marketing, 1(4), 365-372.
Cao, J., Xia, T., Li, J. X. J., Zhang. Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection, Neuro computing, 72(7-9), 1775-1781.
Duan, W., Yu, Y., Cao, Q., & Levy, S. (2015). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282-296.
Cenni, I., & Goethals, P. (2017). Negative hotel reviews on TripAdvisor: A cross-linguistic analysis. Discourse Context Media, 16, 22–30.
Gang R., & Hong T. H. (2017). Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach, Sustainability, 1765 (1-18).
Grant, S., Cordy, J. R., & Skillicorn, D. B. (2013). Using heuristic to estimate an appropriate number of latent topics in source code analysis, Science of Computer Programming, 78(9), 1663-1678.
Gupta, V., & Lehal, G. S. (2009). A Survey of Text Mining Techniques and Applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
Han, S. S. (2017). Effect on Brand Loyalty in Omni-Channel:Focus on Category Knowledge. Journal of Distribution Science 15(3), 61-72.
Hoang B. H. L., Chi T. N., Thi T. H. T., & Thi T. P. N. (2020). Factor Affecting Customers’ Decision to Use Mobile Banking Service: A Case of Thanh Hoa Province, Vietnam. Journal of Asian Finance, Economics and Business, 7(2), 205-212.
Homburg, C., Vollmayr, J., & Hahn. A. (2014). Firm Value Creation Through Major Channel Expansions: Evidence from an Event Study in the United States, Germany and China. Journal of Marketing, 78 (3), 38–61.
Khan, R. A., & Kanth, S. (2016). Text Mining: Knowledge Discovery from Unstructured data, Artificial Intelligent Systems and Machine Learning, 8(2), 71-77.
Kim, H. S., & Shim, J. H. (2017). The Effects of Service Qualities on Customer Satisfaction and Behavioral Intention in Coffee Shops. International Journal of Industrial Distribution &Business, 10(10), 35-43.
Kim, T. H., Park, B. J., & Suh, E. K. (2018). Irregular Big Data Analysis and Considerations for Civil Complaint Based on Design Thinking. International Journal of Industrial Distribution & Business, 9(8), 51-60.
Lee, J. W., Becker, K., & Potluri, R. M. (2019). Antecedents of Corporate Adoption of Social Media and the Role of the Technology Acceptance Model in the Path. The Journal of Asian Finance, Economics and Business, 3(2), 67-76.
Lee, S. Y., & Lee H. S. (2020). A Study on the Smart Tourism Awareness through Bigdata Analysis. Journal of Industrial Distribution & Business, 11 (5), 45-52.
Liu B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge.
Marrese-Taylor, E., Velasquez, J. D., & Bravo-Marquez, F. (2014). A Novel Deterministic Approach for Aspect-based Opinion Mining in Tourism Products Reviews. Expert Systems with Applications, 41(17), 7764-7775.
Niu, L. X., & Lee, J. H. (2018). The Intention of Repurchase on eService Quality by Online Travel Agency Site. International Journal of Industrial Distribution & Business, 9(7). 61-70.
Oh, H. S., & Cheon, H. S. (2018). The Impact of Nature of Purchase and Purchase Utility on Purchase Intention According to Retail entertainment. Journal of Distribution Science, 16 (12), 57-68.
Park, S. H. (2016). Effect of Omni-Channel Use and CustomerBrand Relationship. Journal of Distribution Science, 14(11), 129-138.
Pearce, PL., & Wu, M. Y. (2018). Entertaining International Tourists: An Empirical Study of an Iconic Site in China. Journal of Hospitality & Tourism Research, 42(5), 772-792.
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
Rigby, D. (2011). The Future of Shopping. Harvard Business Review, 89(12), 65–76.
Ryu, J. S. (2019). Consumer Characteristics and Shopping for Fashion in the Omni-channel Retail Environment. Journal of Business, Economics and Environmental Studies, 9(4), 15-22.
Trong N. P., Truc V. H., Phuong V. Le, & Hoang (2020). Factors Affecting the Behavioral Intention and Behavior of Using E–Wallets of Youth in Vietnam. Journal of Asian Finance, Economics and Business 7(10), 295–302.
Verhoef, P. C., Scott A., Neslin, S. A., & Vroomen, B. (2007). Multi-Channel Customer Management: Understanding the Research Shopper Phenomenon. International Journal of Research in Marketing, 24(2), 129-148.
Verhoef, P. C. (2012) Multi-Channel Customer Management Strategy in Handbook of Marketing Strategy. Shankar V. and Carpenter G., eds. Cheltenham: Edward Elgar Publishing Limited.
Yang, S. K., Shim, J. H. (2018). The Effects of Service Qualities on Customer Satisfaction, Trust, and Behavioral Intention in Smartphone Shopping Malls. International Journal of Industrial Distribution & Business. 9(12). 31-43.
Yim, D. S., & Han, S. S. (2016). Omni-channel's Perceived Ease of Use and Perceived Usefulness Effect on Omni-channel Use and Customer-Brand Relationship. Journal of Distribution Science, 14(7), 83-90.
Wang, W., & Yum, C. S. (2015). The Effect of Service Quality on Customer Satisfaction of Smart Phone Banking. Journal of KIIT, 13(4), 95-102.
Won, J., & Kim, B. Y. (2020). The Effect of Consumer Motivations on Purchase Intention of Online Fashion - Sharing Platform. Journal of Asian Finance, Economics and Business, 7(6), 197-207.
Zhang, J., Farris, P. W., Irvin, J. W., Kushwaha, T. T., Steenburgh, J., & Weitz, B. A. (2010). Crafting Integrated Multi- Channel Retailing Strategies. Journal of Interactive Marketing, 24(2), 168-180.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations