arXiv:2606. 00084v1 Announce Type: cross Abstract: Online travel platforms generate vast volumes of user-generated hotel reviews, offering rich opportunities to understand traveler experiences at scale.
By Dineth Jayakody, Pasindu Thenahandi, Sampath Jayarathna
arXiv:2609.36194v1 Announce Type: new
Abstract: Extracted sentiment directions can vary across samples even when downstream sentiment classification remains accurate. To evaluate direction reproducib...
By Muhammad Abdullahi Said, Abass Oguntade, Elisha Komolafe, Babangida Sani, Fatima Muhammad Adam, Muhammad Sammani Sani
arXiv:2607. 10825v1 Announce Type: cross Abstract: Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns.
By Fabrizio Marozzo, Stefano Iannicelli
The paper presents a statistically rigorous sentiment index for Google Play user reviews, combining normalized star ratings and text-sentiment scores through covariance-aware inverse-variance weighting. It aggregates review-level estimates using bounded helpfulness and recency weights, then applies Gaussian-conjugate shrinkage toward a population mean based on estimated precision. The authors also provide distributional diagnostics for different API sort orders, avoid inappropriate Kolmogorov‑Smirnov tests for discrete data, and use a Kalman filter to smooth temporal trends, all supported by full mathematical proofs.
By Marco Mandap
arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.
By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek
arXiv:2504. 14053v2 Announce Type: replace-cross Abstract: Rating systems on accommodation platforms suffer from a familiar problem: nearly every listing displays a nearly perfect score, so the number that is supposed to separate good listings from bad ones barely varies.
By Ali Safari