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Review helpfulness as a function of Linguistic Indicators


Hamad, MSI Malik, Khalid Iqbal


Vol. 18  No. 1  pp. 234-240


Online reviews are playing an important role in customer’s decision making procedure for buying any product online. As buying products online is becoming customer’s first choice while shopping. It is very helpful to make purchase decision for any product by reading online reviews related to that particular product. However, such a large volume of online reviews that is being generated can be considered as a big data challenge for both entities i.e. e-commerce websites and customers. These online reviews are usually ranked on the basis of helpful votes. This article examined the important factors that contribute to the helpfulness of online reviews and built a helpfulness predictive model for online reviews. Five novel linguistic characteristics are proposed and popular machine learning algorithms are applied to construct an effective predictive model for review helpfulness. LCM and visibility features are also used as baseline. We have performed experimental analysis on two popular Amazon review datasets and results reveals that hybrid set of features deliver the best predictive performance. We also found that the proposed Linguistic features are better predictors for review helpfulness as a standalone model. The findings of our study can provide new wisdom to e-commerce vendors for effective ranking of online reviews on the basis of their helpfulness. This research would also help customers in making better decisions before purchasing any product.


Online reviews, Helpfulness, Random forest, Noun, Amazon, Linguistic.