Aspect-Based Sentiment Analysis of Reviews for Pandawa Beach Using Naive Bayes and SVM Methods
DOI:
https://doi.org/10.30871/jaic.v9i2.9083Keywords:
Sentiment analysis, Google Maps, Support Vector Machine (SVM), Naive Bayes, Pandawa BeachAbstract
The presence of digital technology, especially online platforms such as Google Maps, has changed the way people search for information about tourist destinations, including reviews and ratings from previous visitors. Aspect-based sentiment analysis becomes a very useful tool to understand people's views and feelings towards a place or product based on the reviews given and identify aspects of interest to tourists visiting Pandawa Beach, by utilizing Naive Bayes and Support Vector Machine (SVM) methods. The main objective of this research is to identify sentiment patterns based on aspects such as attraction, accessibility, amenities, and ancillary. Data was collected and labeled according to sentiment and aspects, then processed using preprocessing techniques, extracted by bag-of-words method, and chi-square feature selection. The model evaluation results showed that SVM produced the highest F1-Score value of 79,625%, while the Naive Bayes method reached 73.29%.
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