With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products...With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature＇s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.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 e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature＇s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.
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.
