An Investigation on the User Behavior in Social Commerce Platforms: A Text Analytics Approach

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

2 Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

3 Assistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.


Nowadays, the tourism industry accounts for approximately 10% of the global GDP, while it only contributes 3% of the economy in Iran. Since the pressure of US sanctions increases day after day on the Iranian economy, the necessity of paying attention to this industry as a source of foreign currency is felt more than ever. The purpose of this research is to analyze the reviews of users of social commerce websites by using a combination of text mining and data mining techniques. For this purpose, the database of TripAdvisor website ( was evaluated, and all profile information of users who commented on hotels in Iran was collected. These comments on all the content of the website, such as hotels, restaurants, and attractions, were then extracted and analyzed. The optimal number of clusters was considered four clusters by calculating the Davies-Bouldin index, namingly water therapy tourists, boutique hotels style and Iran urban tourists, travelholics and food tourists, business and health tourists. Every single cluster possesses unique attributes and features. Afterward, the association rules were further identified for each cluster according to the characteristics of each cluster and the information in the users' profiles. Finally, a solution is proposed to increase the participation of the users on the website, and targeted promotional plans are expressed in accordance with the well-known features of each cluster.


Afrizal, A. D., Rakhmawati, N. A., &Tjahyanto, A. (2019). New Filtering Scheme Based on Term Weighting to Improve Object Based Opinion Mining on Tourism Product Reviews. Procedia Computer Science, 161, 805-812.
Annisa, R., &Surjandari, I. (2019). Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation. Procedia Computer Science, 161, 739-746.
Chang, T., Hsu, P. Y., Cheng, M. S., Chung, C. Y., & Chung, Y. L. (2015, June). Detecting fake review with rumor model—Case study in hotel review. In International Conference on Intelligent Science and Big Data Engineering (pp. 181-192). Springer, Cham.
Cho, E., & Son, J. (2019). The effect of social connectedness on consumer adoption of social commerce in apparel shopping. Fashion and Textiles, 6(1), 14.
Curty, R. G., & Zhang, P. (2011). Social commerce: Looking back and forward. Proceedings
Dickinger, A., &Lalicic, L. (2016). An analysis of destination brand personality and emotions: A comparison study. Information Technology & Tourism, 15(4), 317-340.
Emarketer. (2019). Internet to Hit 3 Billion Users in 2015,
Hajli, M. N. (2012). An integrated model for e-commerce adoption at the customer level with the impact of social commerce. International Journal of Information Science and Management (IJISM), 77-97.
Hajli, M. N., Shanmugam, M., Powell, P., & Love, P. E. (2015). A study on the continuance participation in on-line communities with social commerce perspective. Technological Forecasting and Social Change.
Leitner, P., &Grechenig, T. (2008). Collaborative shopping networks: Sharing the wisdom of crowds in E-commerce environments. BLED 2008 Proceedings, 21.
Lu, Y., Zhao, L., & Wang, B. (2010). From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention. Electronic Commerce Research and Applications, 9(4), 346-360.
Mangold, W. G., &Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
National Internet Development Agency of Korea. (2008). Social Software: Beyond Consumer, Go Enterprise.
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.
Qi, M., Li, X., Zhu, E., & Shi, Y. (2017). Evaluation of perceived indoor environmental quality of five-star hotels in China: An application of online review analysis. Building and Environment, 111, 1-9.
Ridings, C. M., &Gefen, D. (2004). Virtual community attraction: Why people hang out online. Journal of ComputerÔÇÉMediated Communication, 10(1), 00-00.
Saundage, D., & Lee, C. Y. (2011, January). Social commerce activities–a taxonomy. In ACIS 2011: Identifying the information systems discipline: Proceedings of the 22nd Australasian Conference on Information Systems. ACIS.
Turban, E., Whiteside, J., King, D., & Outland, J. (2017). Introduction to electronic commerce and social commerce. Springer.
Villeneuve, H., & O'Brien, W. (2020). Listen to the guests: Text-mining Airbnb reviews to explore indoor environmental quality. Building and Environment, 169, 106555.
Wirth, R., &Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (pp. 29-39).
Zwass, V. (2010). Co-creation: Toward a taxonomy and an integrated research perspective. International Journal of Electronic Commerce, 15(1), 11-48.