mlsanityData to 7 Oct 2026

Yelp Dataset Challenge: Review Rating Prediction

arXiv 1605.05362 ↗First posted 17 May 2016cs.CL cs.IR cs.LGcited by 84

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Abstract (arXiv)

Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2016.

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Citing papers per year: 2021: 5; 2022: 16; 2023: 13; 2024: 20; 2025: 17; 2026: 13.

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