SENTIMENT EXTRACTION FROM CONSUMER REVIEWS FOR PROVIDING PRODUCT RECOMMENDATIONS

1 .- 21 . (canceled)

22 . A recommendation system comprising a memory storing computer readable program code for recommending a product to a user, wherein the recommendation system is configured to execute the computer readable program code to:

receive a search query from a user device associated with the user;

determine a product feature of the search query;

determine a product corresponding to the product feature, wherein determining the product corresponding to the product feature comprises applying a semantic template to the search query, and wherein the semantic template is applied to the search query by a span query;

retrieve a product profile corresponding to the product;

retrieve a quotation associated with the product and the product feature, wherein the quotation comprises expression of a sentiment about the product or the product feature; and

output a product recommendation comprising the product profile and the quotation to the user device.

23 . The recommendation system of claim 22 , wherein the recommendation system comprises a recommendation database and applying the semantic template to the search query comprises:

building the semantic template from the search query, the semantic template comprising atomic semantic templates corresponding to the product feature; and

searching the recommendation database with the semantic template.

24 . The recommendation system of claim 23 , wherein a plurality of product features, a plurality of sentiments, a plurality of quotations, and a plurality of sentiment volatilities are stored in the recommendation database.

25 . The recommendation system of claim 24 , wherein the recommendation system is further configured to execute the computer readable program code to determine a score for each product feature of the plurality of product features stored in the recommendation database.

26 . The recommendation system of claim 23 , wherein the atomic semantic templates are an unordered set of atomic semantic templates.

27 . The recommendation system of claim 23 , wherein determining each score comprises using the plurality of sentiments, wherein the plurality of sentiments are extracted from a plurality of documents.

28 . The recommendation system of claim 27 , wherein at least one of the plurality of documents is a web page of a manufacturer website or retailer website, the web page including a description of the product.

29 . The recommendation system of claim 28 , wherein the web page further includes one or more consumer comments about the product.

30 . The recommendation system of claim 27 , wherein the recommendation system is further configured to execute the computer readable program code to determine an overall sentiment for each document of the plurality of documents.

31 . The recommendation system of claim 30 , wherein the recommendation system is further configured to execute the computer readable program code to determine a sentiment volatility of the overall sentiment for each document of the plurality of documents.

32 . The recommendation system of claim 31 , wherein the recommendation system is further configured to execute the computer readable program code to determine a reliability score for each document of the plurality of documents.

33 . The recommendation system of claim 32 , wherein the reliability score for each document is based on the sentiment volatility of each document.

34 . The recommendation system of claim 23 , wherein building the semantic template from the search query comprises:

extracting one or more product features from the search query; and

sorting the extracted one or more product features based on a lexicography.

35 . The recommendation system of claim 22 , wherein outputting the product recommendation comprises:

sorting a plurality of product profiles and quotations returned by the search query; and

identifying the product recommendation from the sorted search product profiles and quotations.

36 . The recommendation system of claim 35 , wherein the plurality of product profiles and quotations are sorted based on product feature scores.

37 . The recommendation system of claim 22 , wherein the search query is received from the user device, via a network, by a front end server of the recommendation system.

38 . The recommendation system of claim 22 , wherein the product profile comprises a product name and one or more product ratings associated with the product.

39 . The recommendation system of claim 22 , wherein the quotation is extracted from a previously received document associated with the product.

40 . The recommendation system of claim 22 , wherein the product feature comprises an item characteristic of a product.

41 . The recommendation system of claim 22 , wherein the product feature comprises an abstract characteristic of a product.