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Large Language Models Powered Aspect-Based Sentiment Analysis for Enhanced Customer Insights

Mariana Água et al.

Tourism & Management Studies2025https://doi.org/10.18089/tms.20250101article
ABDC C
Weight
0.53

What the paper says

In the age of social networks, user-generated content has become vital for organizations in tourism and hospitality. Traditional sentiment analysis methods often struggle to process large volumes of data and capture implicit sentiments. This study examines the potential of Aspect-Based Sentiment Analysis (ABSA) using Large Language Models (LLMs) to enhance sentiment analysis. By employing GPT-4o via ChatGPT, we benchmark three approaches: a fuzzy logic-based method, manual human analysis, and a new ChatGPT-based analysis. We analyze a dataset of 500 all-inclusive hotel reviews, comparing these methods to assess ChatGPT's effectiveness in identifying nuanced language and handling subjectivity. The findings reveal a high similarity between ChatGPT and human analysis, showcasing ChatGPT’s ability to interpret complex sentiments and automate sentiment classification tasks. This study highlights the potential of LLMs in transforming customer feedback analysis, providing deeper insights, and improving responsiveness in the hospitality industry. These results contribute to academia by presenting a framework for using LLMs in ABSA and guiding future applications and development.

8 citations

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https://doi.org/https://doi.org/10.18089/tms.20250101

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@article{mariana2025,
  title        = {{Large Language Models Powered Aspect-Based Sentiment Analysis for Enhanced Customer Insights}},
  author       = {Mariana Água et al.},
  journal      = {Tourism & Management Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.18089/tms.20250101},
}

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Large Language Models Powered Aspect-Based Sentiment Analysis for Enhanced Customer Insights

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Evidence weight

0.53

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.70 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.