Key product review extraction based on the RLRSA-CL algorithm

Abstract

Online product reviews on e-commerce platforms help consumers evaluate and select products. However, the large number of accumulated product reviews leads to information overload and redundancy. It is difficult for consumers to quickly obtain needed information. Therefore, this study proposes a new model for extracting key reviews from the perspectives of supporting consumer purchase decisions and reducing review information overload. First, patterns of contextual information surrounding product attributes are extracted to model the product features, questions, and reviews in the same semantic space. Second, the new key review extraction problem is defined as a multi-objective optimization model. Five objective functions in terms of supporting consumer purchase decisions and reducing review information overload are defined in the model. Product attribute weights and product question weights are designed to construct the objective functions. Third, a binary multi-objective reptile search algorithm RLRSA-CL integrating reinforcement learning and cross-learning strategies is innovatively constructed to solve the optimization model and obtain key reviews. Finally, experiments on a real dataset from Jingdong Mall are performed. The results show that the proposed RLRSA-CL algorithm is valid and superior to other algorithms in key review extraction.