A Study on Diversification of Online Product Reviews

Abstract

Recent studies have boosted the e¬commerce industry which has resulted in increased significance of online product reviews. However, this usefulness of product reviews has also attracted the people who try to manipulate overall product perception by generating fake reviews. Another challenge due to boost in e¬commerce is the information overload which is caused by generation of huge reviews data. This study paves a complete pathway by presenting techniques for removal of spam reviews and by proposing a novel algorithm to retrieve a diversified subset of reviews to reduce the burden of information overload. A diversified set of reviews attempts to cover maximum features of the selected product within a limited number of reviews that ultimately leads to reduction in decision time as well as enhances the credibility and reliability for the user. Spam detection techniques were formulated based on deep learning models whereas novel SENTIMENT AND FEATURE ORIENTED DIVERSIFICATION (SeFOD) algorithm was constructed on the features addressed in each review and the sentiments of the review separately. The proposed models showed prominent results and achieved a maximum spam accuracy of 95.78%, 96.38% and 96.18% for LSTM, GRU and CNN models re spectively. The same results were validated on Yelp hotel reviews dataset. Whereas a new measure for calculating the diversity of the reviews set was adopted named as DivScore. The score nearer to 0 means there is no diversity in the set and hence all the retrieved reviews contain similar features. The far this score goes from 0, the more diversity exists in the diversified set. A DivScore of 7.14 was achieved for the selected product from Daraz reviews dataset while 10.88 was the score when a product was diversified from Yelp reviews dataset. This study can be used by e¬commerce industry to maximize their profits as well as is equally relevant for the general users to better choose relevant product for them