Abstract
Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.
| Original language | English |
|---|---|
| Pages (from-to) | 346-355 |
| Number of pages | 10 |
| Journal | Journal of Business Research |
| Volume | 70 |
| DOIs | |
| State | Published - 1 Jan 2017 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2016 Elsevier Inc.
Keywords
- Helpfulness
- Online user reviews
- Product features
- Product ranking
- Text mining
ASJC Scopus subject areas
- Marketing