Social Media Mood Shift Detection via Breakpoint Analysis
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Solution Overview
Problem
Conventional methods for analyzing mood shifts and trends of social media users rely on human subject matter experts, resulting in qualitative and subjective assessments that lack scientific rigor and objectivity.
Innovation Solution
A quantitative system that categorizes textual messages into mood-related word categories, calculates intensity scores, determines breakpoints to minimize square errors, and interprets these breakpoints to identify mood shifts and trends, providing an objective and scientifically rigorous analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods based on human subject matter experts are used to analyze mood shifts, then qualitative insights can be obtained, but scientific rigor and objectivity are compromised
Solution Approach 1:
The patent replaces the mechanical system of human expert analysis with an automated computational system that uses text categorization, score calculation, and breakpoint detection algorithms to objectively measure mood shifts in social media data, thereby eliminating subjectivity while maintaining analytical capability
Solution Approach 2:
The patent transforms qualitative mood assessments into quantitative parameters by calculating intensity scores for different word categories and detecting breakpoints in time series data, converting subjective expert judgments into measurable, objective metrics that can be systematically analyzed
2Measurement precision
If quantitative methods are implemented to improve objectivity, then measurement precision increases, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: text categorization into mood-related word categories, calculation of intensity scores for each category, detection of breakpoints in time series data, and interpretation of breakpoints as mood shifts. This segmentation reduces overall system complexity by making each component independently manageable
Solution Approach 2:
The patent employs parameter changes by optimizing the number and positions of breakpoints to minimize sum of squared errors, transforming the complex breakpoint detection problem into an optimization task with clear mathematical criteria that can be solved systematically
Data Source
AI summary
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.