Perspective data analysis and management

• US 10,546,005 B2
• Filed: 04/17/2019
• Issued: 01/28/2020
• Est. Priority Date: 09/24/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Clark, Adam T., Huebert, Jeffrey K., Payton, Aspen L., Petri, John E.
• Primary Examiner(s): Dorvil, Richemond
• Assistant Examiner(s): Chavez, Rodrigo A
• Application Number:
  US16/386,492
• Publication Number:
  US 20190243850A1
• Time in Patent Office: 286 Days
• Field of Search: 704 9
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A system and computer implemented method for managing perspective data is disclosed. The method may include collecting a first lot of perspective data for an item. The method may include introducing a variant feature to the item to constitute a modified item. The method may include collecting a second lot of perspective data for the modified item. The method may also include evaluating the first and second lots of perspective data to ascertain a sentiment fluctuation based on information relevant to the variant feature.

Claims

1. A system, having one or more computer processors, for managing reviews with respect to a change to an item, the system configured to:

identify a variant feature of an item having a first set of reviews, wherein the variant feature is a changed characteristic of the item, and wherein identifying the variant feature comprises;

parsing the first set of reviews using a natural language processing technique configured to analyze semantic and syntactic content,

determining sentiment factors for two or more reviews from the first set of reviews,

evaluating the first set of reviews to identify a change in sentiment factor between the two or more reviews, and

identifying the variant feature based on the change in sentiment factor;

group, based on the variant feature, the first set of reviews into a first group and a second group, wherein grouping the first set of reviews comprises;

analyzing, using natural language processing, the first set of reviews to determine whether reviews included in the first set of reviews are more closely associated with a first variable element of the variant feature or a second variable element of the variant feature,

sorting into the first group, based on the analyzing, first reviews of the first set of reviews that are associated with the first variable element of the variant feature, and

sorting into the second group, based on the analyzing, second reviews of the first set of reviews that are associated with the second variable element of the variant feature;

determine a first set of relevancy scores for the first group and a second set of relevancy scores for the second group, wherein a relevancy score is a numerical value indicating a relative significance of a review;

establish, using at least one of the first set of relevancy scores and the second set of relevancy scores, a second set of reviews configured to be a subset of the first set of reviews, wherein the second set of reviews is a subset of the first set of reviews that have the relevancy score greater than a threshold relevancy score; and

provide a visual representation of the second set of reviews to a user.

2. The system of claim 1, wherein evaluating the first set of reviews to identify the change in sentiment factor comprises:

determining the change in the sentiment factor in response to a triggering event, and

wherein identifying the variant feature based on the change in sentiment factor comprises;

selecting the triggering event as the variant feature.

3. The system of claim 1, wherein:

the change in the sentiment factor is an increase in the sentiment factors greater than a sentiment change threshold.

4. The system of claim 1, wherein:

the change in the sentiment factor is a decrease in the sentiment factors greater than a sentiment change threshold.

5. The system of claim 1, wherein the first variable element is associated with a first sentiment factor greater than a sentiment threshold, wherein the sentiment threshold is a predetermined sentiment factor, and the second variable element is associated with a second sentiment factor less than the sentiment threshold.

6. The system of claim 1, wherein the first variable element is associated with a first sentiment factor equal to a sentiment threshold, and the second variable element is associated with a second sentiment factor less than the sentiment threshold.

7. The system of claim 1, wherein the determining whether reviews included in the first set of reviews are more closely associated with a first variable element of the variant feature or a second variable element of the variant feature includes using a frequency analysis to determine whether terms or phrases related to the first variable element or the second variable element are mentioned with greater frequency in the reviews.

8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a first computing device, causes the first computing device to:

identify a variant feature of an item having a first set of reviews, wherein the variant feature is a changed characteristic of the item, and wherein identifying the variant feature comprises;

parsing the first set of reviews using a natural language processing technique configured to analyze semantic and syntactic content,

determining sentiment factors for two or more reviews from the first set of reviews,

evaluating the first set of reviews to identify a change in sentiment factor between the two or more reviews, and

identifying the variant feature based on the change in sentiment factor;

group, based on the variant feature, the first set of reviews into a first group and a second group, wherein grouping the first set of reviews comprises;

analyzing, using natural language processing, the first set of reviews to determine whether reviews included in the first set of reviews are more closely associated with a first variable element of the variant feature or a second variable element of the variant feature,

sorting into the first group, based on the analyzing, first reviews of the first set of reviews that are associated with the first variable element of the variant feature, and

sorting into the second group, based on the analyzing, second reviews of the first set of reviews that are associated with the second variable element of the variant feature;

determine a first set of relevancy scores for the first group and a second set of relevancy scores for the second group, wherein a relevancy score is a numerical value indicating a relative significance of a review;

establish, using at least one of the first set of relevancy scores and the second set of relevancy scores, a second set of reviews configured to be a subset of the first set of reviews, wherein the second set of reviews is a subset of the first set of reviews that have the relevancy score greater than a threshold relevancy score; and

provide a visual representation of the second set of reviews to a user.

9. The computer program product of claim 8, wherein evaluating the first set of reviews to identify the change in sentiment factor comprises:

determining the change in the sentiment factor in response to a triggering event, and

wherein identifying the variant feature based on the change in sentiment factor comprises;

selecting the triggering event as the variant feature.

10. The computer program product of claim 8, wherein the change in the sentiment factor is an increase in the sentiment factors greater than a sentiment change threshold.

11. The computer program product of claim 8, wherein the change in the sentiment factor is a decrease in the sentiment factors greater than a sentiment change threshold.

12. The computer program product of claim 8, wherein the first variable element is associated with a first sentiment factor greater than a sentiment threshold, wherein the sentiment threshold is a predetermined sentiment factor, and the second variable element is associated with a second sentiment factor less than the sentiment threshold.

13. The computer program product of claim 8, wherein the first variable element is associated with a first sentiment factor equal to a sentiment threshold, and the second variable element is associated with a second sentiment factor less than the sentiment threshold.