Abstract
Within the emerging context of Web 2.0 social media, online customer reviews are playing an increasingly important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. The sheer volume of customer reviews on the web produces information overload for readers. Developing a system that can automatically identify the most helpful reviews would be valuable to businesses that are interested in gathering informative and meaningful customer feedback. Because the target variable---review helpfulness---is continuous, common feature selection techniques from text classification cannot be applied. In this article, we propose and investigate a text mining model, enhanced using the Regressional ReliefF (RReliefF) feature selection method, for predicting the helpfulness of online reviews from Amazon.com. We find that RReliefF significantly outperforms two popular dimension reduction methods. This study is the first to investigate and compare different dimension reduction techniques in the context of applying text regression for predicting online review helpfulness. Another contribution is that our analysis of the keywords selected by RReliefF reveals meaningful feature groupings.
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