  We use over 350,000 Yelp reviews on 5,000 restaurants to perform an ablation
study on text preprocessing techniques. We also compare the effectiveness of
several machine learning and deep learning models on predicting user sentiment
(negative, neutral, or positive). For machine learning models, we find that
using binary bag-of-word representation, adding bi-grams, imposing minimum
frequency constraints and normalizing texts have positive effects on model
performance. For deep learning models, we find that using pre-trained word
embeddings and capping maximum length often boost model performance. Finally,
using macro F1 score as our comparison metric, we find simpler models such as
Logistic Regression and Support Vector Machine to be more effective at
predicting sentiments than more complex models such as Gradient Boosting, LSTM
and BERT.
