# Yelp Review Rating Prediction: Machine Learning and Deep Learning Models

arXiv: https://arxiv.org/abs/2012.06690 · HTML: https://mlsanity.com/paper/2012.06690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification](https://mlsanity.com/paper/2504.18884/) (2025)
- [Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation](https://mlsanity.com/paper/2407.13069/) (2024)
- [Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques](https://mlsanity.com/paper/2405.11775/) (2024)
- [Generative Distribution Prediction: A Unified Approach to Multimodal Learning](https://mlsanity.com/paper/2502.07090/) (2025)
- [Sentiment Analysis: Predicting Yelp Scores](https://mlsanity.com/paper/2201.07999/) (2022)

## Abstract (arXiv)

We predict restaurant ratings from Yelp reviews based on Yelp Open Dataset. Data distribution is presented, and one balanced training dataset is built. Two vectorizers are experimented for feature engineering. Four machine learning models including Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine are implemented. Four transformer-based models containing BERT, DistilBERT, RoBERTa, and XLNet are also applied. Accuracy, weighted F1 score, and confusion matrix are used for model evaluation. XLNet achieves 70% accuracy for 5-star classification compared with Logistic Regression with 64% accuracy.