Optimizing Latent Space Representation for Tourism Insights: A Metaheuristic Approach.

In the modern digital era, social media platforms with travel reviews significantly influence the tourism industry by providing a wealth of information on consumer preferences and behaviors. However, these textual reviews' complex and varied nature poses analytical challenges. This research employs advanced Natural Language Processing (NLP) techniques to process and analyze vast amounts of travel data efficiently, tackling the challenges posed by the diverse and detailed content in the tourism field. We have developed an innovative text clustering methodology that combines BERT's deep linguistic analysis capabilities (Bidirectional Encoder Representations from Transformers) with the thematic organization strengths of LDA (Latent Dirichlet Allocation). This hybrid model, further refined with the dimensionality reduction capabilities of ELMAE and the optimization precision of PPSO (Phasor Particle Swarm Optimization), yields concise, contextually enriched text representations. Such refined data representations enhance the accuracy of K-means clustering, facilitating nuanced topic identification within the complex domain of travel reviews. This approach streamlines feature extraction and ensures rapid training and minimal loss, underscoring the model's effectiveness in distilling and reconstructing textual features. Our application of this hybrid LDA-BERT model to analyze TripAdvisor reviews of Thailand's shopping destinations reveals meaningful insights, significantly aiding in understanding customer experiences. Despite its contributions, this study acknowledges limitations, including biases in usergenerated content and the intricacies of accurately interpreting sentiments and contexts within reviews. This research marks a significant step forward in utilizing NLP for tourism industry analysis, providing a pathway for future investigations to build upon. [ABSTRACT FROM AUTHOR]