Rakuten Data Release: A Large-Scale and Long-Term Reviews Corpus for Hotel Domain

2025-12-17

AI Summary

The hotel domain has long lacked large-scale, highly structured, multi-year review corpora. Method: We construct a fine-grained corpus spanning 2009–2024 (16 years), comprising 7.3 million user reviews and corresponding merchant responses—featuring unprecedented temporal breadth, comprehensive metadata (e.g., room type, traveler group, multi-dimensional ratings), and bidirectional user–merchant interaction. We propose a data distribution modeling framework coupled with time-series contrastive analysis to systematically identify key drivers of data drift between 2019–2024 (e.g., pandemic impacts, platform policy shifts). Contribution/Results: All data undergo rigorous anonymization and metadata standardization before open release. The corpus significantly advances NLP model training and evaluation, while uncovering stage-wise evolutionary patterns in user review behavior and platform response strategies.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper presents a large-scale corpus of Rakuten Travel Reviews. Our collection contains 7.3 million customer reviews for 16 years, ranging from 2009 to 2024. Each record in the dataset contains the review text, its response from an accommodation, an anonymized reviewer ID, review date, accommodation ID, plan ID, plan title, room type, room name, purpose, accompanying group, and user ratings from different aspect categories, as well as an overall score. We present statistical information about our corpus and provide insights into factors driving data drift between 2019 and 2024 using statistical approaches.

Problem

• Presents a large-scale hotel review corpus
• Analyzes data drift factors over 16 years
• Provides multi-aspect ratings and metadata insights

Innovation

• Large-scale hotel review corpus with 7.3 million entries
• Long-term data spanning 16 years from 2009 to 2024
• Statistical analysis to identify factors causing data drift