Topic Modeling of Raja Ampat Tourism on TripAdvisor Sites Using Latent Dirichlet Allocation

In response to the burgeoning interest in tourist destination sentiment analysis, this study focuses on Indonesia’s renowned Raja Ampat. Our primary objective is to employ Latent Dirichlet Allocation (LDA), a topic modeling technique, to delve into sentiments expressed in a corpus of 5,227 TripAdvisor documents concerning Raja Ampat. Leveraging the Vader library for data preprocessing, our analysis reveals a dominant trend of positivity, with 80% of sentiments classified as positive, while 15.8% are neutral, and 4.2% are negative. Through systematic experimentation with Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) feature representations, two major topics emerge: ‘Environmental Tourism’ and ‘Snorkeling & Services.‘ Remarkably, the LDA model attains coherence scores of 0.6005 (BoW) and 0.4148 (TF-IDF), underscoring the robustness of our analysis. These findings not only contribute valuable insights into the prevailing sentiments and dominant topics associated with Raja Ampat tourism but also offer practical implications for the industry. The tourism sector in Raja Ampat can leverage these insights to enhance services and experiences, ultimately facilitating sustainable tourism development in this ecologically vital region. This research thus serves as a significant step toward promoting informed decision-making and improving tourist experiences in Raja Ampat and similar destinations, fostering sustainable growth and preservation of natural beauty.