Details

Autor(en) / Beteiligte

• Taherkhani, Leila
• Daneshvar, Amir
• Amoozad Khalili, Hossein
• Sanaei, Mohamad Reza
• A. Yousefi, Afshar
• Afshar A Yousefi

Titel

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Ist Teil von

• Advances in Civil Engineering, 2023-08, Vol.2023, p.1-8

Ort / Verlag

New York: Hindawi

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The ability of hotels to differentiate themselves from competitors and continue to operate profitably depends on their ability to retain their customers by building long-term and permanent customer relationships. Technological developments in recent years have made it possible for companies to predict their customers’ behavior by accessing their opinions faster and preventing them from churning. Managing customer churn prediction projects has become an important issue today, especially in the hotel industry. Therefore, this research seeks to analyze projects that predict the churn of hotel customers to provide a model to help hotel managers in this field. In this research, an approach based on text mining on customers’ comments in the Persian language is presented, which uses the random forest algorithm for classification that was considered the most effective method to solve this problem. In this model, to increase the efficiency of the proposed method in compare with existing works, the gravitational search algorithm was used to select the useful features, and the differential evolution algorithm was used to adjust the parameters of the classification method. The dataset of this research is the collected data from the customer database on social networks and hotels’ websites, especially the hotels on Kish Island in Iran. The results of this research showed that after the implementation of the preprocessing operations, the method of adjusting the parameters and removing the unimportant features, the model’s accuracy increased significantly. The precision, recall, F1, and accuracy criteria were 0.77, 0.76, 0.76, and 0.77, respectively.