Sentiment Analysis of TikTok Comments on Ambon Tourism Destinations Using the Naïve Bayes Algorithm - Lady Angelic Pattipeilohy; de Fretes , Riko; Makaruku , Yoakhina Nicole; Jermias Victor Manuhutu; Latuny , Wilma (MUKASI: Jurnal Ilmu Komunikasi, 2026)
Tourism promotion through social media has become an effective strategy in increasing public interest in tourist destinations. One of the popular platforms used by the public to share opinions is TikTok, where users actively leave comments related to tourism content. This study aims to analyze public sentiment toward Ambon tourism destinations based on TikTok comments using the Naïve Bayes classification algorithm. The dataset used in this research was obtained through a web scraping process and consisted of 115 comments that had been preprocessed through case folding, tokenization, stopword removal, and stemming. The comments were categorized into three sentiment classes: positive, neutral, and negative. The experimental results show that the Naïve Bayes model produced an accuracy value of 0.50, a precision value of 0.25, a recall value of 0.50, and an F1-score of 0.33. The sentiment distribution showed 75 positive comments, 25 neutral comments, and 15 negative comments. Although the classification performance remains moderate, the findings indicate that public sentiment toward Ambon tourism tends to be dominated by positive and neutral opinions. This research provides an initial reference for tourism stakeholders in evaluating public perceptions and improving digital tourism promotion strategies.