Comparative Sentiment Analysis Integrating Large Language Models for Company Website Reviews

Music City Center 

Customer feedback particularly plays an important role for tourism, affecting travelers' decisions when choosing accommodations. Researcher in this tourism industry tends to choose sentiment analysis techniques such as Naïve Bayes Approach, Latent Dirichlet Allocation (LDA), Structural Topic Model (STM) to help host find the pivotal factors contributing to the reputation of their property. However, as the existence of sarcasm, implicit sentiment and contextual ambiguity always, it makes the classification inaccurately. To address these limitations, we propose leveraging Large Language Models (LLMs) to rephrase customer reviews before utilizing sentiment analysis methods. After compared to the methods without the rephrasing process of feedback by using LLMs, it proves that the hybrid methodology incorporating LLMs significantly enhance the performance of LDA and STM, providing a more accurate and reliable classification and interpretation of customer reviews.

Sentiment Analysis

Large Language Model

LDA

STM 

International Chinese Statistical Association