Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews
This work addresses a specific problem for Yelp users by providing incremental improvements in extracting restaurant features from reviews.
The paper tackled the problem of insufficient restaurant aspect information on Yelp by using an SVM-based sentiment analysis method to identify features like environment, service, and flavor from reviews, finding that customers express more sentiment about service and that results align with common sense for different cuisines.
Many people use Yelp to find a good restaurant. Nonetheless, with only an overall rating for each restaurant, Yelp offers not enough information for independently judging its various aspects such as environment, service or flavor. In this paper, we introduced a machine learning based method to characterize such aspects for particular types of restaurants. The main approach used in this paper is to use a support vector machine (SVM) model to decipher the sentiment tendency of each review from word frequency. Word scores generated from the SVM models are further processed into a polarity index indicating the significance of each word for special types of restaurant. Customers overall tend to express more sentiment regarding service. As for the distinction between different cuisines, results that match the common sense are obtained: Japanese cuisines are usually fresh, some French cuisines are overpriced while Italian Restaurants are often famous for their pizzas.