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.
خلاصه ماشینی:
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.
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.
With the development of Web 200 and the rise of social commerce websites, word of mouth marketing has been converted to an electronic form, by which the users on social networks express their point of views and experiences on products, services, and companies; thus, their reviews may affect other people on those social networks to buy/not buy the products or services.
The present article analyzes user reviews on social commerce websites on the basis of text mining and data mining techniques on TripAdvisor.
The main problem is that many users only play a passive role in the social commerce websites and do not participate in them by writing reviews about their experiences to help other users increase the usefulness of the website and select proper services or products.
Literature Review Social Commerce Some researches show that potential customers are more interested in other people's opinions and suggestions compare to the information and explanations provided by product makers or service providers (Turban et al.
The present study applied association rules for the first time to describe each user's cluster attributes and recommend some marketing solution to increase users' participation and selling volume for tourism-related businesses.