Naïve Bayes–Based Chatbot with Sentiment Analysis for Culinary Preferences in Bali - Widhiyanti, Anak Agung Sandatya; Sekarini, I Gusti Agung Ayu (Politeknik Ganesha Medan, 2025)
The rapid growth of digital technology has increased the adoption of chatbots across industries, including the culinary and tourism sectors. However, existing systems often lack integration of customer sentiment and user preferences, limiting recommendation relevance. This study develops a personalized chatbot by combining sentiment analysis of Google Maps reviews with user taste preferences for traditional Balinese cuisine. A dataset of 5,000 reviews was analyzed using the Naïve Bayes classifier, achieving 88% accuracy. User evaluation with 100 respondents showed positive perceptions of usability and engagement, though recommendation suitability scored lower. The findings highlight the potential of sentiment-driven personalization and suggest future improvements through advanced models, larger datasets, and multilingual features for tourism.