With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring di...With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.展开更多
Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to gene...Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method.展开更多
文摘With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.
基金supported by the National Natural Science Foundation of China under Grant Nos.60973104 and 60803075with the aid of a grant from the International Development Research Center,Ottawa,Canada IRCI Project
文摘Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method.
