Redundant online reviews often have a negative impact on the efficiency of consumers' decisi on-making in their on line shopping.A feasible solution for business analytics is to select a review subset from the ori...Redundant online reviews often have a negative impact on the efficiency of consumers' decisi on-making in their on line shopping.A feasible solution for business analytics is to select a review subset from the original review corpus for consumers,which is called review selection.This study aims to address the diversified review selection problem,and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection(SA-DRS)that considers the semantic relationship of review features and the con tent diversity of selected reviews simultaneously.SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation(LDA)topic model and the Word2vec model to measure the topic relation and word context relation.Based on the established feature taxonomy,the similarity between each pair of reviews is defined and the review quality is estimated as well.Fin ally,diversified,high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method,forming the selected review subset.Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches.展开更多
文摘Redundant online reviews often have a negative impact on the efficiency of consumers' decisi on-making in their on line shopping.A feasible solution for business analytics is to select a review subset from the original review corpus for consumers,which is called review selection.This study aims to address the diversified review selection problem,and proposes an effective review selection approach called Simulated Annealing-Diversified Review Selection(SA-DRS)that considers the semantic relationship of review features and the con tent diversity of selected reviews simultaneously.SA-DRS first constructs a feature taxonomy by utilizing the Latent Dirichlet Allocation(LDA)topic model and the Word2vec model to measure the topic relation and word context relation.Based on the established feature taxonomy,the similarity between each pair of reviews is defined and the review quality is estimated as well.Fin ally,diversified,high-quality reviews are selected heuristically by SA-DRS in the spirit of the simulated annealing method,forming the selected review subset.Extensive experiments are conducted on real-world e-commerce platforms to demonstrate the effectiveness of SA-DRS compared to other extant review selection approaches.
