SYSTEM AND METHOD FOR AGGREGATING AND SUMMARIZING PRODUCT/TOPIC SENTIMENT

• US 20090319342A1
• Filed: 06/17/2009
• Published: 12/24/2009
• Est. Priority Date: 06/19/2008
• Status: Abandoned Application

• Current Assignee: WIZE Commerce Incorporated
• Original Assignee: WIZE, Inc. (Nextag Incorporated)
• Inventors: Chandran, Rajesh, Shilman, Michael
• Application Number:
  US12/486,344
• Publication Number:
  US 20090319342A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Documents are collected from a variety of publicly available sources that contain product data including product reviews, product specifications, price information and the like. Snippets of text obtained from the documents are analyzed for relevance, sentiment, credibility and other aspects that help evaluate the quality of a product. Feature vectors are computed for snippets to analyze relevance, sentiment, or credibility. Statistical analysis is performed on the feature vectors to estimate a measure of the relevance, sentiment, or credibility. Factors associated with various snippets are aggregated to compute a quality score for a product or a particular aspect of product including product features, attributes, usages, or user personas. Information is displayed on a user interface that allows the user to examine the details relevant to computation of the quality score.

Claims

1. A computer implemented method for ranking a plurality of products with respect to a topic, the method comprising:

receiving documents aggregated from multiple online information sources wherein each document contains information on products;

computing snippets of text from the documents, wherein each snippet contains a portion of text describing a product with respect to the topic;

determining an estimate of relevance of each snippet to the topic;

determining an estimate of sentiment of each snippet with respect to the topic; and

determining an aggregate quality score for each products in the plurality of products for ranking the products based on factors associated with each snippet comprising the estimate of relevance of the snippet, the estimate of sentiment of the snippet and the estimate of credibility of the snippet.

2. The computer implemented method of claim 1, further comprising:

determining an estimate of credibility of each snippet.

3. The computer implemented method of claim 1, further comprising:

displaying information used for determining the ranking of products with respect to the topic.

4. The computer implemented method of claim 1, wherein determining an estimate of relevance of each snippet comprises:

identifying a subset of the plurality of snippets wherein each snippet contains terms describing the topic;

computing a feature vector for each snippet wherein the feature vector components are determined based on relevance of the snippet to the topic; and

determining a relevance score for each snippet based on statistical analysis of the feature vectors associated with the snippets.

5. The computer implemented method of claim 4, wherein computing a feature vector for a snippet comprises:

selecting a pattern based on the topic; and

determining a component of the feature vector based on how well the pattern matches the snippet.

6. The computer implemented method of claim 5, wherein the pattern based on the topic comprises at least one of:

a regular expressions of terms describing the topic;

an n-gram with terms describing the topic, wherein the n-gram is selected based on the frequency of occurrence of the n-gram in the plurality of snippets;

an n-gram with terms describing the topic, wherein the n-gram is selected based on the frequency of occurrence of the n-gram in the plurality of snippets and the n-gram is annotated with part-or-speech information;

a boolean predicate based on product metadata;

a scalar value based on the length of the snippet;

number of instances of a phrase in the snippet; and

a measure of proximity of a phrase to the start or the end of the snippet.

7. The computer implemented method of claim 1, wherein determining an estimate of sentiment of each snippet comprises:

identifying a subset of the plurality of snippets wherein each snippet contains terms describing the topic;

computing a feature vector for each snippet wherein the feature vector components are determined based on sentiment described in the snippet; and

determining a sentiment score for each snippet based on statistical analysis of the feature vectors associated with the snippets.

8. The computer implemented method of claim 7, wherein the subset of the plurality of snippets is identified based on relevance of each snippet to the topic.

9. The computer implemented method of claim 7, wherein computing a feature vector for a snippet comprises:

determining a component of the feature vector based on a criteria determining the sentiment described in the snippet.

10. The computer implemented method of claim 9, wherein criteria for determining the sentiment described in the snippet comprises at least one of:

matching of an n-gram with terms describing sentiments, wherein the n-gram is selected based on the frequency of occurrence of the n-gram in the plurality of snippets;

matching of an n-gram with terms describing sentiments, wherein the n-gram is selected based on the frequency of occurrence of the n-gram in the plurality of snippets and the n-gram is annotated with part-or-speech information; and

an estimate of the overall quality of the product; and

an estimate of the credibility of the snippet.

11. The computer implemented method of claim 2, wherein determining an estimate of credibility of each snippet is based on one or more factors, comprising:

a measure of credibility of author of the snippet;

a measure of credibility of the source from where the snippet is obtained;

number of helpfuls and unhelpfuls associated with the snippet; and

the size of a post associated with the snippet.

12. The computer implemented method of claim 1, wherein the factors associated with each snippet further comprise:

an age of the snippet.

13. The computer implemented method of claim 2, wherein the aggregate quality score of the product with respect to the topic is determined as a aggregate value of an estimate of vote corresponding to each snippet, wherein the vote is indicative of the quality of the product with respect to the topic as determined by the snippet.

14. The computer implemented method of claim 13, wherein the vote corresponding to each snippet is determined as a product of terms comprising a relevance score determined based on the estimate of relevance raised to power of a first constant, a sentiment score determined based on the estimate of sentiment raised to power of a second constant, and a credibility score determined based on the estimate of credibility raised to power of a fourth constant.

15. The computer implemented method of claim 14, wherein the product of terms further comprises a term corresponding to a function of the age of the snippet.

16. The computer implemented method of claim 13, wherein the vote corresponding to each snippet is determined as a weighted sum of terms comprising a relevance score determined based on the estimate of relevance raised to power of a first constant, a sentiment score determined based on the estimate of sentiment raised to power of a second constant, and a credibility score determined based on the estimate of credibility raised to power of a fourth constant.

17. The computer implemented method of claim 16, wherein the weighted sum of terms further comprises a term corresponding to a function of the age of the snippet.

18. A computer-implemented system for ranking a plurality of products with respect to a topic, the system comprising:

a computer processor; and

a computer-readable storage medium storing computer program modules configured to execute on the computer processor, the computer program modules comprising;

an aggregation module configured to;

receive documents aggregated from multiple online information sources wherein each document contains information on products;

compute snippets of text from the documents, wherein each snippet contains a portion of text describing a product with respect to the topic;

a relevance analyzer module configured to;

determine an estimate of relevance of each snippet to the topic;

a sentiment analyzer module configured to;

determine an estimate of sentiment of each snippet with respect to the topic; and

a quality score computation module configured to;

determine an aggregate quality score for each products in the plurality of products for ranking the products based on factors associated with each snippet comprising the estimate of relevance of the snippet, the estimate of sentiment of the snippet and the estimate of credibility of the snippet.

19. A computer program product having a computer-readable storage medium storing computer-executable code for ranking a plurality of products with respect to a topic, the code comprising:

an aggregation module configured to;

receive documents aggregated from multiple online information sources wherein each document contains information on products;

compute snippets of text from the documents, wherein each snippet contains a portion of text describing a product with respect to the topic;

a relevance analyzer module configured to;

determine an estimate of relevance of each snippet to the topic;

a sentiment analyzer module configured to;

determine an estimate of sentiment of each snippet with respect to the topic; and

a quality score computation module configured to;

determine an aggregate quality score for each products in the plurality of products for ranking the products based on factors associated with each snippet comprising the estimate of relevance of the snippet, the estimate of sentiment of the snippet and the estimate of credibility of the snippet.