Summary of Analyzing Consumer Reviews For Understanding Drivers Of Hotels Ratings: An Indian Perspective, by Subhasis Dasgupta et al.
Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective
by Subhasis Dasgupta, Soumya Roy, Jaydip Sen
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to analyze consumer reviews of Indian hotels and identify the key factors influencing their ratings. The study leverages web scraping methods to collect data, followed by Latent Dirichlet Allocation for topic extraction and sentiment analysis for aspect-specific sentiment mapping. Random Forest is then employed to determine the importance of these aspects in predicting final user ratings. This research contributes to our understanding of consumer preferences in the hospitality sector. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers looked at what people are saying about hotels online. They wanted to know what matters most when customers decide how good a hotel is. To do this, they collected lots of reviews from the internet and used special tools to figure out what people are talking about. They also tried to understand how happy or unhappy people were with different things like cleanliness or food. The goal was to see which aspects are most important in helping hotels get high ratings. |
Keywords
* Artificial intelligence * Random forest
