New information search model for online reviews with the perspective of user requirements

Many e-commerce websites currently provide online reviews to share e-shoppers’ experience with the products. To help e-shoppers obtaining information efficiently, these websites usually summarize product information based on their certain predefined aspects. However, e-shopper’s aspects should be annotated to make sure that more highly related information of online reviews can be fetched for fulfilling e-shopper’s requirements. Hence, this study integrates an annotation approach with similarity techniques (Keyword pair similarity and Aspect-sentence similarity) to propose a new framework to fetch more highly correlated sentences for e-shoppers. Experimental results show that most of the combinations in the proposed approach have high prediction performance in the Top 10 sentences with Precision (0.90 or higher).