Granted Patent B1

US 10,929,605 · App. 14/613,324 · Granted Feb 23, 2021

Inventors: Lisa Joy Rosner (Emerald Hills, CA); Jens Erik Tellefsen (Los Altos, CA); Michael Jacob Osofsky (Palo Alto, CA); Jonathan Spier (Menlo Park, CA); Ranjeet Singh Bhatia (Sunnyvale, CA); Malcolm Arthur De Leo (Pleasanton, CA); Karl Long (Lyndhurst, GB)

Assignee: NetBase Solutions, Inc.

G06F40/30G06F16/35G06F40/10

Abstract

Claims (97)

1. A method for graphically characterizing statements about an object, comprising:

applying, as a result of computing hardware and programmable memory, frame extraction, to a first corpus, in order to attempt to identify, for each statement of the corpus, an object and a sentiment expressed about the object;

identifying, as a result of computing hardware and programmable memory, a first object-specific corpus, that is a subset of the first corpus, where all the statements of the first object-specific corpus are about a same first object;

categorizing, as a result of computing hardware and programmable memory, a sentiment of each statement, of the first object-specific corpus;

determining, as a result of computing hardware and programmable memory, a net metric as a function of the categorization; and

producing, as a result of computing hardware and programmable memory, a first graphical representation, of the first object-specific corpus, that is placed, relative to a first axis, according to the net metric.

2. The method of claim 1 , wherein the categorizing further comprises:

categorizing a polarity of each statement, of the first object-specific corpus.

3. The method of claim 2 , wherein the categorizing further comprises:

categorizing an intensity of each statement, of the first object-specific corpus.

4. The method of claim 3 , wherein the step of determining a net metric further comprises:

determining a net polarity measure, as a function of the polarity categorization; and

determining a net intensity measure, as a function of the intensity categorization.

5. The method of claim 4 , wherein the step of producing a first graphical representation further comprises:

placing the first graphical representation, relative to the first axis, in accordance with the net polarity measure; and

placing the first graphical representation, relative to a second axis, in accordance with the net intensity measure.

6. The method of claim 1 , wherein the step of producing a first graphical representation further comprises:

producing a graphical representation having an area proportional to a number of statements contained in the first object-specific corpus.

7. The method of claim 3 , wherein the categorizing further comprises:

categorizing the polarity as either positive or negative; and

categorizing the intensity as either strong or weak.

8. The method of claim 5 , further comprising:

production of a first and a second dividing line, each dividing line parallel to, respectively, the first axis and the second axis.

9. The method of claim 8 , wherein production, of the first and second dividing lines, further comprises:

placing the first and second dividing lines in order to divide the area, for placement of the first graphical representation of the first object-specific corpus, into quadrants.

10. The method of claim 1 , further comprising:

applying frame extraction, to a second corpus, in order to attempt to identify, for each statement of the corpus, an object and a sentiment expressed about the object;

identifying a first plurality of object-specific corpuses, each of which is a subset of the second corpus, where all the statements, of an object-specific corpus, are about a same object;

categorizing a sentiment of each statement of each object-specific corpus of the first plurality of object-specific corpuses;

determining a net metric, for each object-specific corpus of the first plurality of object-specific corpuses, as a function of the categorization; and

determining a first median net metric measure, from the net metrics of the first plurality of object-specific corpuses; and

placing a first median dividing line, perpendicular to the first axis, located at, approximately, the first median net metric measure.

11. The method of claim 8 , further comprising:

applying frame extraction, to a second corpus, in order to attempt to identify, for each statement of the corpus, an object and a sentiment expressed about the object;

identifying a first plurality of object-specific corpuses, each of which is a subset of the second corpus, where all the statements, of an object-specific corpus, are about a same object;

categorizing a polarity and intensity of each statement of each object-specific corpus;

determining a net polarity measure and a net intensity measure, for each object- specific corpus, as a function of the categorization; and

determining a median net polarity measure, from the net polarity measures;

determining a median net intensity measure, from the net intensity measures; and

placing each of the first and second dividing lines, respectively, at a location that approximately intersects the median net intensity and median net polarity measures.

12. The method of claim 8 , further comprising:

identifying a first plurality of object-specific corpuses, each of which is a subset of the first corpus, where all the statements, of an object-specific corpus, are about a same object;

categorizing the polarity and intensity of each statement of each object-specific corpus;

determining a net polarity measure and a net intensity measure, for each object- specific corpus, as a function of the categorization;

selecting a first subset of the first plurality of object-specific corpuses;

placing the first and second dividing lines in order to divide an area, relative to the first subset, into quadrants for placement of a graphical representation for each member of the first subset.

13. The method of claim 1 , wherein the first object represents a brand.

14. The method of claim 12 , wherein each object, for each of the first plurality of object-specific corpuses, represents a different brand but all the brands are part of a same category of brand.

15. The method of claim 1 , further comprising:

producing a second corpus, containing statements within a second temporal range that is different from a first temporal range of the statements of the first corpus;

applying frame extraction, to the second corpus, in order to attempt to identify, for each statement of the corpus, an object and a sentiment expressed about the object;

identifying a second object-specific corpus, that is a subset of the second corpus, where all the statements of the second object-specific corpus are about the same first object as the first object-specific corpus;

categorizing the sentiment of each statement, of the second object-specific corpus;

determining a second net metric value as a function of the categorization of the second object-specific corpus; and

producing a second graphical representation, of the second object-specific corpus, that is placed, relative to the first axis, according to the second net metric.

16. The method of claim 15 , wherein the second temporal range is disjoint from the first temporal range.

17. The method of claim 15 , wherein a beginning and ending time, of the first and second temporal ranges, are the same.

18. The method of claim 15 , wherein producing a second graphical representation further comprises:

updating a position of the first graphical representation.

19. The method of claim 15 , wherein producing a second graphical representation further comprises:

producing a graphical representation additional to the first graphical representation.

20. The method of claim 15 , wherein producing a second graphical representation further comprises:

placing the second graphical representation relative to a second temporal axis, with the first graphical representation occupying a differing position relative to the second temporal axis.

21. The method of claim 1 , further comprising:

categorizing the polarity of each statement, of the first object-specific corpus, wherein the first object-specific corpus contains mostly statements from social media; and

determining a net polarity measure, as a function of the polarity categorization, wherein the net polarity measure has a strong correlation with a survey-based technique for determining sentiment.

22. The method of claim 21 , wherein the survey- based technique is the American Consumer Satisfaction Index.

23. The method of claim 21 , wherein the strong correlation corresponds to a value of 0 . 7 or greater, for a correlation coefficient.

24. The method of claim 4 , wherein the net polarity and intensity measures have a very low correlation.

25. The method of claim 24 , wherein the very low correlation corresponds to a value of 0 . 1 or less, for a correlation coefficient.

26. The method of claim 1 , further comprising:

categorizing the polarity of each statement, of the first object-specific corpus; and

determining a net polarity measure of the first object-specific corpus, as a function of the polarity categorization, wherein the net polarity measure has a very low correlation to a number of statements contained in the first object-specific corpus.

27. The method of claim 26 , wherein the very low correlation corresponds to a value of 0 . 1 or less, for a correlation coefficient.

28. The method of claim 5 , further comprising:

identifying a first plurality of object-specific corpuses, each of which is a subset of the first corpus, where all the statements, of an object-specific corpus, are about a same object and the first object-specific corpus is part of the first plurality of object-specific corpuses;

categorizing the polarity and intensity of each statement of each object-specific corpus;

determining a net polarity measure and a net intensity measure, for each object- specific corpus, as a function of the categorization;

determining a total number of statements across the first plurality of object- specific corpuses;

producing a first relative value, of a first number of statements contained in the first object-specific corpus considered relative to the total number of statements;

producing the first graphical representation of the first object-specific corpus with an area proportional to the first relative value.

29. The method of claim 28 , wherein the first relative value has a very low correlation to the net polarity measure.

30. The method of claim 29 , wherein the very low correlation corresponds to a value of 0 . 1 or less, for a correlation coefficient.

31. The method of claim 1 , further comprising:

identifying a first plurality of object-specific corpuses, including the first object- specific corpus, where each object-specific corpus is a subset of the first corpus;

limiting each corpus, of the first plurality of object-specific corpuses, such that all statements are about a same object and a same characteristic of the same object;

categorizing the polarity and intensity of each statement of each object-specific corpus;

determining a net polarity measure and a net intensity measure, for each object- specific corpus, as a function of the categorization;

placing a graphical representation, of each corpus of the first plurality of object- specific corpuses, relative to first and second axes, in accordance with, respectively, the net polarity and intensity measures;

dividing a graphing space, defined by the axes, into quadrants;

mapping each quadrant to representing one of strengths, weaknesses, opportunities and threats.

32. The method of claim 11 , wherein the second corpus is the same as the first corpus and the first object-specific corpus is a member of the first plurality of object-specific corpuses.

33. The method of claim 1 , further comprising:

producing a first Logical Form semantic representation for a first statement of the first corpus;

determining whether a first frame extraction rule matches the first Logical Form;

producing, if the first frame extraction rule matches, a first instance having at least object and sentiment roles, with values of the first Logical Form assigned to corresponding roles of the first instance; and

including the first instance as part of the identifying of the first object-specific corpus.

Assignments (10)

Continuity (1)