Sentiment Analysis of Restaurant Reviews on Yelp with Incremental Learning

Abstract

Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutions where training data is collected before the model is built. If we want to avoid retraining the entire model from time to time, incremental learning becomes the best alternative solution for this task. In this work, we present a variant of online random forests to perform sentiment analysis on customers' reviews. Our model is able to achieve accuracy similar to offline methods and comparable to other online models.