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

Abstract Number:

3711

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Speed

Participants:

Bong-Jin Choi (1), Gaoya Tu (2), Jing Bai (3)

Institutions:

(1) North Dakota State University, N/A, (2) NDSU, N/A, (3) North Dakota State University Main Campus, N/A

Co-Author(s):

Gaoya Tu
NDSU

Jing Bai
North Dakota State University Main Campus

Speaker:

Bong-Jin Choi
North Dakota State University

Abstract Text:

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 the Large Language Model(AI) with sentiment analysis and LDA-based topic modeling for online reviews to explore what customer 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.

Keywords:

Sponsors:

Korean International Statistical Society

Tracks:

Miscellaneous

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