Linguistic features for review helpfulness prediction

• Presents novel linguistic features for predicting helpfulness of online reviews.
• Describes a method to automatically extract linguistic features from review text.
• Builds a predictive model and empirically evaluates it on amazon review datasets.
• The model is found to be a better predictor of helpfulness for experience goods.

Online reviews play a critical role in customer’s purchase decision making process on the web. The reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper examines the factors that contribute towards helpfulness of online reviews and builds a predictive model. The proposed predictive model extracts novel linguistic category features by analysing the textual content of reviews. In addition, the model makes use of review metadata, subjectivity and readability related features for helpfulness prediction. Our experimental analysis on two real-life review datasets reveals that a hybrid set of features deliver the best predictive accuracy. We also show that the proposed linguistic category features are better predictors of review helpfulness for experience goods such as books, music, and video games. The findings of this study can provide new insights to e-commerce retailers for better organization and ranking of online reviews and help customers in making better product choices.