Profiling users' behavior, and identifying important features of review 'helpfulness'

The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people’s attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

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