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An Investigation on the User Behavior in Social Commerce Platforms: A Text Analytics Approach | ||
| Journal of Information Technology Management | ||
| دوره 12، شماره 4، 2020، صفحه 180-199 اصل مقاله (234.62 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2020.296648.2458 | ||
| نویسندگان | ||
| Amir Arzy1؛ Mohammad Taghi Taghavifard* 2؛ Zohreh Dehdashti Shahrokh2؛ Iman Raeesi Vanani3 | ||
| 1Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran. | ||
| 2Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran. | ||
| 3Assistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran. | ||
| چکیده | ||
| 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 (TripAdvisor.com) 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. | ||
| کلیدواژهها | ||
| Social commerce؛ TripAdvisor؛ Social media؛ Text mining؛ Data mining | ||
| مراجع | ||
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