4.6 Article

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Journal

SUSTAINABILITY
Volume 11, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/su11195254

Keywords

eWOM (electronic Word-of-Mouth); machine learning; marketing strategy; sentiment analysis; review helpfulness

Funding

  1. National Natural Science Foundation of China [71602125]
  2. Start-Up Research Grant for Newly Recruited Faculty [00191965271305]
  3. Fundamental Research Funds for the Central Universities [JBK1801039]

Ask authors/readers for more resources

Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naive Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers' needs and maintain sustainable competitive advantages.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available