Online reviews play a critical role in customers purchase decision making process on the web. The online reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper aims to study the factors that contribute towards helpfulness of online reviews and build a predictive model. It introduces a set of novel features for predicting review helpfulness. The proposed model is validated on two real-life review datasets to demonstrate its utility. A rigorous experimental evaluation also reveals that the proposed linguistic features are good predictors of review helpfulness.