Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews

TL;DR

This paper presents a machine learning approach using SVM to analyze Yelp reviews, extracting sentiment-based features to characterize restaurant aspects like environment, service, and cuisine type, providing more detailed insights than overall ratings.

Contribution

The study introduces a novel SVM-based method to identify specific restaurant features from reviews, enhancing understanding of customer sentiments across different restaurant types.

Findings

Customers express more sentiment about service.

Japanese cuisine is associated with freshness.

French cuisines are often perceived as overpriced.

Abstract

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.