Relation Analysis between Hotel Review Rating Scores and Sentiment Analysis of Reviews by Chinese Tourists Visiting Japan

TL;DR

This study investigates the relationship between hotel review ratings and sentiment analysis of reviews by Chinese tourists visiting Japan, using machine learning and statistical correlation methods to assess their alignment.

Contribution

It introduces an entropy-based SVM for sentiment classification and analyzes the correlation between sentiment ratios and review ratings.

Findings

Sentiment classification achieved effective accuracy with the entropy-based SVM.

Positive sentiment ratios correlate strongly with higher review ratings.

The study provides insights into the reliability of ratings as sentiment indicators.

Abstract

In current times, the importance of online hotel review sites has become more and more apparent. Users of these sites reference of reviews strongly influences their purchase behavior and as such, reviews are important to companies and researchers alike. The majority of review sites offer both text reviews and numerical hotel ratings, and both information sources are widely used by researchers as a representation of a customer's sentiment and opinion. However, an opinion is a difficult concept to measure, and as such, depending on the relation these two sources have, it would be apparent whether or not it is safe to consider them equally in research. In this study we utilize an entropy-based Support Vector Machine to classify positive and negative sentiments in hotel reviews from the site Ctrip, then calculating the ratio of positive and negative sentiment in each review and examine their correlation with said review's rating score using Spearman and Kendall Correlation coefficients and Maximal Information Coefficient (MIC).