Aspect-Based Sentiment Analysis of Borobudur Temple Reviews Use Support Vector Machine Algorithm

Abstract

As one of the most popular tourist attractions in Indonesia, Borobudur Temple is currently included in the top ten list of tourism priorities by the Ministry of Tourism. To increase the number of tourists, it is very important to maintain the quality of tourist attractions. Tourist growth is directly related to the number of online reviews of tourist attractions. Tourism managers need more than just reviewing good and negative sentiments to maintain and improve the quality of tourist attractions. Many aspects serve as benchmarks for visitors to come to a tourist spot, such as aspects of ticket prices, location, attractiveness, facilities, accessibility, visual image, and human resources. Therefore, sentiment analysis is needed for each of these aspects to find out aspects that need to be improved in order to increase the number of visitors. Support Vector Machine (SVM) is an algorithm used to categorize aspect-based sentiments. analyzed using SVM, the dataset must first be cleaned and normalized through pre-processing. The results of the analysis show that the aspects of accessibility and visual image need to be improved to maintain and increase the number of visitors. This is because these two aspects have the most negative reviews compared to other aspects. The results of model testing only get an average accuracy value of 0.8148 because the distribution of data for all aspects and reviews is not balanced.