Sentiment Analysis: Predicting Yelp Scores
This work addresses sentiment prediction for restaurant reviews, but it is incremental as it builds on existing methods like BERT.
The paper tackled sentiment analysis of restaurant reviews using the Yelp dataset, showing that deep models with attention mechanisms performed well, and proposed a novel Multi-tasked joint BERT model that improved classification performance.
In this work, we predict the sentiment of restaurant reviews based on a subset of the Yelp Open Dataset. We utilize the meta features and text available in the dataset and evaluate several machine learning and state-of-the-art deep learning approaches for the prediction task. Through several qualitative experiments, we show the success of the deep models with attention mechanism in learning a balanced model for reviews across different restaurants. Finally, we propose a novel Multi-tasked joint BERT model that improves the overall classification performance.