Systems and Methods for Automatic Understanding of Consumer Evaluations of Product Attributes from Consumer-Generated Reviews

• US 20150186790A1
• Filed: 12/31/2014
• Published: 07/02/2015
• Est. Priority Date: 12/31/2013
• Status: Abandoned Application

• Current Assignee: Soshoma, Inc.
• Original Assignee: Soshoma, Inc.
• Inventors: Ehlen, Patrick, Peintner, Bart, Calafiore, Gianmauro
• Application Number:
  US14/587,946
• Publication Number:
  US 20150186790A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Consumer-generated product reviews are a mainstay of electronic commerce and a key factor in the consumer decision process. A shopper'"'"'s typical online purchase decision can involve tedious reading of multiple reviews written by previous purchasers in order to find and compare products for purchase. Disclosed herein is an automated system, and associated methods, that identifies the product attributes discussed in consumer reviews and summarizes consumer sentiment for each attribute for each product thus, offering immense value to consumers and retailers alike.

Claims

1. In a computing environment, a method of extracting from a pool of written reviews of products within a product category the words which describe attributes pertaining to products within that category, comprising

dividing the written reviews into sentences using a sentence segmenter;

tokenizing the words within each sentence;

generating a parse tree for each sentence and extracting nouns, noun-noun combinations, and adjective-noun combinations;

quantifying the frequency of each such noun, noun-noun combination, and adjective-noun combination; and

retaining all high-frequency nouns, noun-noun combinations, and adjective-noun combinations.

2. The method of claim 1, wherein

prior to tokenization, lexicalization of non-compositional combinations or idiomatic phrases is performed.

3. The method of claim 1, wherein

prior to parsing, collocation analysis is performed which compares the frequency of multiple-word-expressions in the written product reviews to their frequency in writings that are not product reviews in order to identify those multiple-word-expressions which are uniquely frequent in the written product reviews;

and

combining those multiple-word-expressions which are uniquely frequent in the written product reviews into single tokens.

4. In a computing environment, for a sentence extracted from a written product review, a method of determining the probability that the sentence is about a specific attribute, comprising

applying at least one mathematical model which correlates the presence and position of words within the sentence to the probability that the sentence is about that specific attribute.

5. The method of claim 4, wherein

the mathematical model was generated using a classifier which compares a pool of sentences known to address the specific attribute against a pool of sentences known not to address the specific attribute to identify words and phrases highly correlated with the sentence addressing the specific attribute and which assigns a weighted value to each such word or phrase based upon its predictive power, and which outputs a score comprising the sum of the weighted value of predictive words or phrases present in the sentence.

6. The method of claim 5, wherein the classifier is selected from the group consisting of:

a context-free grammar model, a hidden Markov model, a vector-space model, a maximum entropy model, a naive Bayes model, a shallow neural net model, and a deep belief net.

7. The method of claim 4, wherein

multiple mathematical models which correlate the presence and position of words within a sentence to the probability that the sentence is about that specific attribute are applied to a sentence;

and

the resulting scores generated by the multiple models are summed to create a score representing the goodness-of-fit of the sentence to the specific attribute.

8. The method of claim 7, wherein

the model assigns weighted values to words and phrases and the output scores are the sum of the values of words and/or phrases present in the sentence.

9. The method of claim 8, wherein

the model output scores are binary yes/no values.

10. The method of claim 7, wherein

the model output scores are weighted such that certain of the multiple models applied to the sentence have a greater impact on the summed score.

11. In a computing environment, a method of determining the polarity of a sentiment expressed in a written sentence or sentence segment which addresses a product attribute, comprising

analyzing the sentence or sentence segment with one or more mathematical models that predict the likelihood that the sentence or sentence segment expresses a sentiment of a selected polarity.

12. The method of claim 11, wherein

the mathematical model was generated using a classifier which compares a pool of sentences known to express the sentiment of the selected polarity about the attribute against a pool of sentences known not to express the sentiment of the selected polarity about the attribute to identify words and phrases highly correlated with the sentence or sentence segment expressing the selected polarity and which assigns a weighted value to each such word or phrase based upon its predictive power, and which outputs a score comprising the sum of the weighted values of predictive words or phrases present in the sentence.

13. The method of claim 12 wherein the classifier is selected from the group consisting of:

a context-free grammar model, a hidden Markov model, a vector-space model, a maximum entropy model, a naive Bayes model, a shallow neural net model, and a deep belief net.

14. The method of claim 11, wherein

the selected polarity is selected from the group consisting of positive, negative, or neutral.

15. The method of claim 11, wherein

multiple mathematical models which that predict the likelihood that the sentence or sentence segment expresses a sentiment of a selected polarity are applied to the sentence or sentence segment;

and

the resulting scores generated by the multiple models are summed to create a score representing the goodness-of-fit of the sentiment of selected polarity to the attribute in the sentence or sentence segment.

16. The method of claim 15, wherein

the model assigns weighted values to words and phrases and the output scores are the sum of the values of words and/or phrases present in the sentence.

17. The method of claim 15, wherein

the model output scores are binary yes/no values.

18. The method of claim 15, wherein

the model output scores are weighted such that certain of the multiple models applied to the sentence have a greater impact on the summed score.