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.

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.