Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find w...Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61170263
文摘Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.
