UCLA

Yelp has been a dominating company in the business rating industry. In the current days, reviews and ratings critically affect people’s decisions. A model that effectively predicts the ratings could be supportive to businesses. In this research, we develop several models based on various NLP and regression methods based on the text review and other traits of the reviews to predict the final star rating of each. Models including LSTM, GRU, BERT, and multinomial logistic regression are utilized. The fine-tuning BERT model has the best result of 68.8% on the testing dataset. Although the model predicts well in general, we could further investigate and evaluate text reviews on multiple aspects.