In this paper, we propose a method for enriching product catalogs, which traditionally include only objective data provided by manufacturers or retailers, with subjective information extracted from reviews written by customers. Our method was designed to associate opinions taken from reviews with the product attributes they refer to. This is done by matching aspect expression identified in opinions with attributes from the product, which we model here as aspect classes. To verify the effectiveness of our method, we executed an extensive experimental evaluation that revealed that customers frequently mention aspects related to product attributes in their reviews. The attributes often receive more mentions than the product itself. Our method consistently reached almost 0.7 of F 1 measure in the task of associating the opinion with the correct attribute (or with the product as a whole), across four product categories, in two different scenarios. These results significantly improved the results achieved by a representative baseline.

中文翻译：

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一种用用户意见丰富产品目录的方面驱动方法

在本文中，我们提出了一种丰富产品目录的方法，该方法传统上仅包括制造商或零售商提供的客观数据，并从客户撰写的评论中提取主观信息。我们的方法旨在将评论中的意见与其所指的产品属性相关联。这是通过将意见中标识的方面表达式与产品的属性进行匹配来完成的，我们在这里将其建模为方面类。为了验证我们方法的有效性，我们进行了广泛的实验评估，结果表明客户在他们的评论中经常提到与产品属性相关的方面。这些属性通常比产品本身获得更多提及。我们的方法始终接近于 0。7 of F 1 在两个不同场景中跨四个产品类别将意见与正确的属性（或与整个产品）相关联的任务中的度量。这些结果显着改善了代表性基线所取得的结果。