Abstract
With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses.
Highlights
The rapid growth of the Internet and the popularity of crowd-sourced review platforms have introduced electronic Word-of-Mouth (e-WoM) communities that provide a massive amount ofUser-Generated Content (UGC), i.e., online product reviews [1,2]
The results showed better performance for the Convolutional Neural Network (CNN) model in comparison with other Machine Learning (ML) models [23]
The top k businesses ranking comparison was done using the following three criteria: (a) The ranking described in Table 3 was based on the star rating and the number of reviews. (b) The ranking based on cumulative helpfulness is given in Table 4. (c) Table 5 shows the ranking of businesses according to the star rating and cumulative helpfulness
Summary
User-Generated Content (UGC), i.e., online product reviews [1,2]. The popular review websites, e.g., Yelp, Amazon, TripAdvisor, IMDB, Yahoo, Google, etc., serve as an essential source of information and help users in evaluating product quality and making purchase decisions [3,4,5,6]. These websites, despite differing, i.e., Yelp reviews business, Amazon is an e-commerce website and review products, TripAdvisor is a booking website, etc., the principle of review helpfulness are common [7]. There are more than 730 million reviews on TripAdvisor [9] and more than 184 million on Yelp [10]
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