Identifying and Forecasting Shifts in the Mood of Social Media Users
First Claim
1. A quantitative method for identifying shifts in a mood of social media users, comprising:
- categorizing textual messages generated from the social media users over a selected period of time into a plurality of word categories, wherein each word category contains a set of words associated with the mood of social media users;
for each word category, calculating a score representing an intensity of the mood of the social media users, wherein a value of the score and its corresponding time point define a data point for the word category;
determining breakpoints in the mood of social media users that minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time, wherein space of all possible breakpoints for the word categories are objectively searched to identify a defined number and locations of the breakpoints; and
interpreting the breakpoints over the selected period of time to identify the shifts in the mood of social media users.
1 Assignment
0 Petitions
Accused Products
Abstract
Quantitatively identifying and forecasting shifts in a mood of social media users is described. An example method includes categorizing the textual messages generated from the social media users over a selected period of time into a plurality of word categories, with each word category containing a set of words associated with the mood of social media users. A score indicating an intensity of the mood of the social media users is calculated for each word category, wherein a value of the score and its corresponding time point define a data point for the word category. Subsequently, breakpoints in the mood of social media users are determined so that the breakpoints minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time. Further, space of all possible breakpoints for the word categories are searched to identify a defined number and locations of the breakpoints. Finally the breakpoints over the selected period of time are interpreted to identify the shifts in the mood of social media users and trends between breakpoints.
24 Citations
34 Claims
-
1. A quantitative method for identifying shifts in a mood of social media users, comprising:
-
categorizing textual messages generated from the social media users over a selected period of time into a plurality of word categories, wherein each word category contains a set of words associated with the mood of social media users; for each word category, calculating a score representing an intensity of the mood of the social media users, wherein a value of the score and its corresponding time point define a data point for the word category; determining breakpoints in the mood of social media users that minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time, wherein space of all possible breakpoints for the word categories are objectively searched to identify a defined number and locations of the breakpoints; and interpreting the breakpoints over the selected period of time to identify the shifts in the mood of social media users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
-
-
19. A system for quantitatively identifying shifts in a mood of social media users, comprising:
-
a message categorizer, configured to categorize textual messages generated from the social media users over a selected period of time into a plurality of word categories, wherein each word category contains a set of words associated with the mood of social media users; a score calculator, configured to, for each word category, calculate a score representing an intensity of the mood of the social media users, wherein a value of the score and its corresponding time point define a data point for the word category; a breakpoint determinator, configured to determine breakpoints in the mood of social media users that minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time, wherein space of all possible breakpoints for the plurality of word categories are objectively searched to identify a defined number and locations of the breakpoints; and a breakpoint interpreter, configured to interpret the breakpoints over the selected period of time to identify the shifts in the mood of social media users. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
-
-
34. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
-
categorizing textual messages generated from the social media users over a selected period of time into a plurality of word categories, wherein each word category contains a set of words associated with the mood of social media users; for each Word category, calculating a score representing an intensity of the mood of the social media users, wherein a value of the score and its corresponding time point define a data point for the word category; determining breakpoints in the mood of social media users that minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time, wherein space of all possible breakpoints for the word categories are searched to identify a defined number and locations of the breakpoints; and interpreting the breakpoints over the selected period of time to identify the shifts in the mood of social media users and the trends between the breakpoints.
-
Specification