Comparing Tourist Perceptions on Google Maps Reviews Using Natural Language Processing: Case Study Of Urban Tourism Sites in Indonesian and Asia-Pacific Cities

SALMAN ALBIR RIJAL, Prof. Ir. Achmad Djunaedi, MUP, Ph.D.

2024 | Skripsi | PERENCANAAN WILAYAH DAN KOTA

This research explores the tourist perceptions of urban tourism destinations in Indonesian and other Asia-Pacific cities through the lens of natural language processing (NLP). The objectives include identifying key terms correlated with tourist sentiments and uncovering unique images associated with these destinations. The methodology involved comprehensive data collection from Google Maps reviews, totaling 5000 reviews per site across 18 urban tourism sites, categorized into historical sites, squares, markets, monuments, amusement parks, and malls. Preprocessing steps, such as translation, stopword removal, and lemmatization, were applied to the collected data before analysis. Word comparison techniques, sentiment analysis, and contextual analysis were employed to extract insights. The findings reveal distinctive patterns and challenges across various urban tourism categories. Cultural heritage sites play a crucial role in preserving local traditions, while recreational spaces serve as vibrant hubs for entertainment and cultural exchange. Markets and malls offer unique shopping experiences, from traditional crafts to luxury goods. Recommendations include enhancing authenticity, addressing challenges like overcrowding and cleanliness, and diversifying attractions to cater to diverse visitor preferences. This study contributes to a sophisticated approach to analyzing visitor perceptions in urban tourism sites, utilizing openly available big data and crowdsourced feedback. The results offer valuable insights for city governments and tourism management to identify strengths, weaknesses, and comparative advantages, thereby fostering sustainable tourism development and enhancing the overall visitor experience.

This research explores the tourist perceptions of urban tourism destinations in Indonesian and other Asia-Pacific cities through the lens of natural language processing (NLP). The objectives include identifying key terms correlated with tourist sentiments and uncovering unique images associated with these destinations. The methodology involved comprehensive data collection from Google Maps reviews, totaling 5000 reviews per site across 18 urban tourism sites, categorized into historical sites, squares, markets, monuments, amusement parks, and malls. Preprocessing steps, such as translation, stopword removal, and lemmatization, were applied to the collected data before analysis. Word comparison techniques, sentiment analysis, and contextual analysis were employed to extract insights. The findings reveal distinctive patterns and challenges across various urban tourism categories. Cultural heritage sites play a crucial role in preserving local traditions, while recreational spaces serve as vibrant hubs for entertainment and cultural exchange. Markets and malls offer unique shopping experiences, from traditional crafts to luxury goods. Recommendations include enhancing authenticity, addressing challenges like overcrowding and cleanliness, and diversifying attractions to cater to diverse visitor preferences. This study contributes to a sophisticated approach to analyzing visitor perceptions in urban tourism sites, utilizing openly available big data and crowdsourced feedback. The results offer valuable insights for city governments and tourism management to identify strengths, weaknesses, and comparative advantages, thereby fostering sustainable tourism development and enhancing the overall visitor experience.

Kata Kunci : urban tourism, tourist perception, natural language processing, Asia-Pacific