AI-Driven Sentiment Analysis and LDA-Based Topic Modeling with Automated Summarization

Thomas M. Menino Convention & Exhibition Center 

Online consumers provide rich, unstructured textual data to express their satisfaction and dissatisfaction. It has motivated researchers to investigate how to systematically process, analyze, and extract meaningful patterns hidden in the large volumes of unstructured text. This study combines a fine-tuned local large language model (AI) with sentiment analysis and LDA-based topic modeling of online reviews to identify what customers truly care about and which factors contribute to positive feedback. This novel collaborative approach improves the interpretability and readability of the large volume of comments, no longer limiting them to explicit sentiment classification before sentiment analysis, and generates concise, readable summary sentences. Using customer reviews as a case, results reveal 10 themes are prominently featured in positive reviews. These findings suggest that both accommodation comfort and positive host–guest interactions increase the likelihood of positive reviews.

Sentiment Analysis

Large Language Model, Fine Tuned AI, Local LLM

Artificial Intelligence

Latent Dirichlet

Multinomial Logistic Regression

Topic Modeling 

Korean International Statistical Society