Abstract: A method and system for automatically ranking product reviews according to review helpfulness. Given a collection of reviews, the method employs an algorithm that identifies dominant terms and uses them to define a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a ‘locally optimal’ review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. In one embodiment a Multi Layer Lexical Model (MLLM) approach partitions the dominant lexical terms in a review into layers, creates a compact unified layers lexicon, and ranks the reviews according to their weight with respect to unified lexicon, all in a fully unsupervised manner. When used to rank book reviews, it was found that the invention significantly outperforms the user votes-based ranking employed by Amazon.

Abstract: A method and system for automatically ranking product reviews according to review helpfulness. Given a collection of reviews, the method employs an algorithm that identifies dominant terms and uses them to define a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a ‘locally optimal’ review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. In one embodiment a Multi Layer Lexical Model (MLLM) approach partitions the dominant lexical terms in a review into layers, creates a compact unified layers lexicon, and ranks the reviews according to their weight with respect to unified lexicon, all in a fully unsupervised manner. When used to rank book reviews, it was found that the invention significantly outperforms the user votes-based ranking employed by Amazon.