Sentiment Analysis of Tourist Reviews at Waterfront City Pangururan Using Naive Bayes and TF-IDF Algorithm

Ichwanul Muslim Karo Karo, Justaman Arifin Karo Karo, Selvia Djasmayena, Rizki Wahyudi

Abstract


This research aims to analyze the sentiment of tourist reviews on Waterfront City Pangururan tourist destination using Naive Bayes algorithm and TF-IDF method. Data was collected from 311 Google Maps reviews, then went through preprocessing stages such as data cleaning, tokenization, stopwords removal, and stemming. A classification model was built using Naive Bayes, with evaluation results showing an accuracy of 90.48% and a predominance of positive sentiments in the reviews. These results indicate that this approach is effective in automatically classifying travelers' opinions. The findings can be a valuable input for managers and local governments in improving the quality of tourism services and attractions.

Keywords


Naive bayes; Sentiment analysis; TF-IDF; Tourist review; Waterfront city Pangururan.

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DOI: https://doi.org/10.17509/seict.v6i1.86210

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