Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models
This work provides practical guidance for sentiment analysis tasks, but it is incremental as it applies existing methods to a new dataset without introducing novel approaches.
The study compared text preprocessing techniques and various machine learning and deep learning models for sentiment analysis on Yelp reviews, finding that simpler models like Logistic Regression and SVM outperformed more complex ones like BERT in terms of macro F1 score.
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.