Perspective data analysis and management

• US 10,042,913 B2
• Filed: 03/27/2015
• Issued: 08/07/2018
• 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): Gills, Kurtis
• Application Number:
  US14/671,208
• Publication Number:
  US 20160085843A1
• Time in Patent Office: 1,229 Days
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

A system and computer-implemented method for managing perspective data is disclosed. The method may include identifying a variant feature of an item having a first set of perspective data. The method may include grouping, based on the variant feature, the first set of perspective data into a first group and a second group. The method may include determining a first set of relevancy scores for the first group and a second set of relevancy scores for the second group. The method may also include establishing, using at least one of the first and second relevancy scores, a second set of perspective data configured to include a subset of the first set of perspective data.

Claims

1. A system for managing perspective data, the system comprising one or more processors communicatively coupled to a memory and configured to:

identify a variant feature of an item having a first set of perspective data, the first set of perspective data including a first set of reviews of the item, using natural language processing to analyze linguistic content of the first set of reviews, wherein the variant feature is a changed feature of the item;

group, based on the variant feature, the first set of perspective data into a first group and a second group using natural language processing to analyze the first set of reviews and determine whether reviews included in the first set of reviews are more closely related to a first variable element of the variant feature or a second variable element of the variant feature using a frequency analysis, wherein grouping the first set of data comprises;

sorting into the first group, first perspective data of the first set of perspective data that is associated with the first variable element of the variant feature, and

sorting into the second group, second perspective data of the first set of perspective data that is 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 by using natural language processing to parse the first group and the second group for content and calculating weights of the parsed content, wherein a relevancy score is a numerical value indicating a relative significance, notability, or quality of a group;

establish, using at least one of the first set of relevancy scores and the second set of relevancy scores, a second set of perspective data configured to include a subset of the first set of perspective data; and

provide a visual representation of the second set of perspective data to a user.

2. The system of claim 1, wherein the variant feature is configured to change with respect to a predetermined fixed criterion.

3. The system of claim 1, wherein identifying the variant feature of the item having a first set of perspective data includes:

parse, using a natural language processing technique configured to analyze semantic and syntactic content, the first set of perspective data;

determine, in response to parsing the first set of perspective data, a set of shared characteristics of the set of perspective data; and

select, from the set of shared characteristics of the first set of perspective data, a first shared characteristic as the variant feature.

4. The system of claim 2, wherein the first variable element and the second variable element correspond to the fixed criterion.

5. The system of claim 1, wherein determining a first set of relevancy scores for the first group includes:

parse, using a natural language processing technique configured to analyze semantic and syntactic content, the first group;

calculate, based on syntactic content, semantic content, and metadata for the first group, the first set of relevancy scores; and

assign the first set of relevancy scores to the first group.

6. The system of claim 1, wherein establishing, using at least one of the first and second relevancy scores, a second set of perspective data configured to be a subset of the first set of perspective data includes:

determine that the first set of relevancy scores of the first group is greater than a first relevancy threshold, and that the second set of relevancy scores of the second group is lower than the first relevancy threshold; and

filter, in response to determining that the second set of relevancy scores of the second group is lower than the first relevancy threshold, the first set of perspective data to remove the second group.

7. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer 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 perspective data, the first set of perspective data including a first set of reviews of the item, using natural language processing to analyze linguistic content of the first set of reviews, wherein the variant feature is a changed feature of the item;

group, based on the variant feature, the first set of perspective data into a first group and a second group using natural language processing to analyze the first set of reviews and determine whether reviews included in the first set of reviews are more closely related to a first variable element of the variant feature or a second variable element of the variant feature using a frequency analysis, wherein grouping the first set of data comprises;

sorting into the first group, first perspective data of the first set of perspective data that is associated with the first variable element of the variant feature, and

sorting into the second group, second perspective data of the first set of perspective data that is 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 by using natural language processing to parse the first group and the second group for content and calculating weights of the parsed content, wherein a relevancy score is a numerical value indicating a relative significance, notability, or quality of a group;

establish, using at least one of the first set of relevancy scores and the second set of relevancy scores, a second set of perspective data configured to include a subset of the first set of perspective data; and

provide a visual representation of the second set of perspective data to a user.

8. The computer program product of claim 7, wherein the variant feature is configured to change with respect to a predetermined fixed criterion.

9. The computer program product of claim 7, further comprising computer readable program code configured to:

parse, using a natural language processing technique configured to analyze semantic and syntactic content, the first set of perspective data;

determine, in response to parsing the first set of perspective data, a set of shared characteristics of the set of perspective data; and

select, from the set of shared characteristics of the first set of perspective data, a first shared characteristic as the variant feature.

10. The computer program product of claim 8, wherein the first variable element and the second variable element correspond to the fixed criterion.