Extract Sentiment and Support Vector Machine (SVM) Performance of Hotel Guest Review Classification
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Yerik Afrianto Singgalen
*
Atma Jaya Catholic University of Indonesia,
Indonesia
(*) Corresponding Author
Abstract
The hotel accommodation business highly depends on consumer preferences regarding products and services. The intensity of hotel guest visits and the level of guest satisfaction with the services provided by hotel management can be seen from various guest reviews on websites used as reservation media. Therefore, this research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method to implement the data mining process using the webharvy application and the machine learning process using the Rapidminer application. Meanwhile, the operators used are Synthetic Minority Over-sampling Technique SMOTE in overcoming data imbalances and sentiment extract operators to obtain a total string score before sentiment labels are determined and processed using the Support Vector Machine (SVM) algorithm. The results of this study showed that SVM without using SMOTE operators resulted in an accuracy value of 95.82%, a precision value of 95.80%, a recall value of 100%, and an Area Under Curve (AUC) value of 0.798 (79.8%). Otherwise, SVM performance using SMOTE operators produces an accuracy value of 92.05%, a precision value of 100%, a recall value of 84.08%, and an Area Under Curve (AUC) value of 99.99 (99.9%). Furthermore, based on ten popular words, hotel guests are concerned about breakfast, staff, pool, room, and hotel. Thus, the guests' highlights are the menu served by the hotel, the service provided by employees, room conditions, and hotel brands. Therefore, hotel management needs to improve the quality of products and services to increase satisfaction and intention to stay again.
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