Skip to main content

Table 2 Summary of the reviewed papers which examine the relationship between numeric ratings and texts reviews in prediction models

From: Text-rating review discrepancy (TRRD): an integrative review and implications for research

Paper

Used Model

Domain

(Manually labelling)

Linking between Experts’ ratings and available rating scores were checked?

(Automatic labelling)

Linking between lexicon-based review ratings and available rating scores were checked?

Degree of consistency between text reviews and rating scores

Zhu et al. [68]

Regression Model

Tourism and hospitality

(Airbnb)

No

Yes

Tobit Models

Coefficients = 0.3072 for positive and –4.2846 for negative

Degree: Weak for positive

Li [29]

Statistical Model

Airlines

No

Yes

“Semantria”

Kendall’s

tau and the Spearman’s rho Correlation

Kendall’s tau = 0.207 and the Spearman’s rho = 0.268

Degree: Weak

Geetha et al. [18]

Naive Bayes Classifier

Hotels

No

Yes

Linear Regression Model

A linear relationship between customer sentiment and rating

R square has a value of 0.21. So, 21% of the variation in the customer ratings is explained by customer sentiment polarity

Degree: Weak

Tsang And Prendergas, 2009 [60]

Statistical Study

Movies

No

Yes

Analysis of covariance (ANCOVAs)

A significant interaction between text and rating valences on trustworthiness

Ganu et al. [16]

Support Vector Machine classifiers (sentiment classification)

Regression (numeric scores)

Restaurants

Yes

Yes

Private lexicon done by researchers

Percentage Analysis

56% of the reviews were annotated as positive and 18% as negative. The associated numeric ratings provided by users pointed that 73% of reviews having positive rating

Degree: Average