1. A computer-implemented method for automatically ranking product reviews relating to a product of interest stored in a computer-readable memory according to the estimated helpfulness of the product reviews, said method comprising: identifying dominant lexical terms in the product reviews; using the dominant lexical terms to define a feature vector representation; converting the product reviews to said feature vector representation; and ranking the product reviews according to the distance of the product reviews from a ‘locally optimal’ review vector; wherein: lexical terms are assigned a dominance score based on statistical occurrence of words in the product reviews and general literature that is not specific to the subject matter of the product reviews; the dominance score of lexical terms that appear in the product reviews and are also commonly found in the general literature are assigned different values compared to lexical terms that appear in the product reviews but are not commonly found in the general literature; and the dominance score Di(t) of a lexical item t in a layer i is given by: Di ⁡ ( t ) = fRi ⁡ ( t ) · c · 1 log ⁢ ⁢ B ⁡ ( t ) where fRi(t) is the frequency of t in Ri, B(t) is the average number of times t appears per one million words in the balanced corpus, and c is a factor used to control the level of dominance; so as to capture key concepts that are dominant in the collection Ri, balancing bias induced by frequent words, capturing words of different semantic types and capturing words that are dominant but infrequent.
1. A computer-implemented method for automatically ranking product reviews relating to a product of interest stored in a computer-readable memory according to the estimated helpfulness of the product reviews, said method comprising: identifying dominant lexical terms in the product reviews; using the dominant lexical terms to define a feature vector representation; converting the product reviews to said feature vector representation; and ranking the product reviews according to the distance of the product reviews from a ‘locally optimal’ review vector; wherein: lexical terms are assigned a dominance score based on statistical occurrence of words in the product reviews and general literature that is not specific to the subject matter of the product reviews; the dominance score of lexical terms that appear in the product reviews and are also commonly found in the general literature are assigned different values compared to lexical terms that appear in the product reviews but are not commonly found in the general literature; and the dominance score Di(t) of a lexical item t in a layer i is given by: Di ⁡ ( t ) = fRi ⁡ ( t ) · c · 1 log ⁢ ⁢ B ⁡ ( t ) where fRi(t) is the frequency of t in Ri, B(t) is the average number of times t appears per one million words in the balanced corpus, and c is a factor used to control the level of dominance; so as to capture key concepts that are dominant in the collection Ri, balancing bias induced by frequent words, capturing words of different semantic types and capturing words that are dominant but infrequent. 6. The method according to claim 1 , when used to rank product reviews.