Hybrid Text Regression Model for Predicting Review Helpfulness BI Congress 3 : Driving Innovation through Big Data

Business intelligence and analytics are playing an increasingly prominent role in many organizations. User-generated content and online social media open up new opportunities for businesses that can exploit and innovate with this new source of Web 2.0 data. In this paper, we concentrate on one important application – predicting the helpfulness of online customer reviews. We frame it as a regression problem and apply text mining techniques. We propose a hybrid feature selection approach, which combines a filter with a wrapper, for a BI text regression problem. Based on online review data collected from Amazon.com, we demonstrate empirically that the hybrid approach produces the best prediction results among all the models examined. This study is the first to develop and validate a hybrid feature selection methodology for text regression in a BI context.