Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908326650970112 |
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| author | Safari, Ali |
| author_facet | Safari, Ali |
| contents | This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions Safari, Ali Computers and Society Artificial Intelligence Computation and Language Machine Learning This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment. |
| title | Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions |
| topic | Computers and Society Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.14053 |