Machine Learning Analysis of Digital Media Content Impact
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Solution Overview
Problem
Existing methods for analyzing the impact of digital media content rely on subjective human analysis, such as focus groups and surveys, which are inefficient and incapable of establishing causal relationships between content features and their effects on opinions, beliefs, or intentions.
Innovation Solution
A method using machine learning models to analyze digital media content by extracting features from both the content and audience responses, determining importance indications, direction of influence, and influence scores, allowing for automated and reproducible analysis of media content impact without human data gathering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual focus groups and surveys are used to analyze media content impact, then human subjective analysis can be obtained, but the process requires extensive manual input from multiple users and cannot establish causal relationships
Solution Approach 1:
The patent replaces manual human analysis (mechanical system of focus groups and surveys) with an automated machine learning system that processes media content and audience responses computationally. This substitution eliminates the need for extensive manual input while enabling precise causal relationship detection through algorithmic analysis of content features and their correlation with audience opinions, beliefs, and intentions.
Solution Approach 2:
The system enables self-service analysis by automatically processing media content through feature extraction, training machine learning models, and generating impact predictions without requiring human researchers to manually conduct focus groups or surveys. The automated pipeline independently completes the entire analysis workflow from content input to causal relationship identification.
2Productivity
If automated machine learning analysis is implemented, then productivity and reproducibility are improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex analysis system into distinct functional modules: feature extraction from media content, feature extraction from audience responses, machine learning model training, and impact prediction. This segmentation manages system complexity by organizing the automated workflow into manageable, independent components that can be developed and maintained separately while achieving high productivity through their integrated operation.
3Quantity of substance
If traditional survey methods are used, then human experience and subjectivity are captured, but the process is time-consuming and requires many different users
Solution Approach 1:
The patent uses machine learning models to learn from and copy the analytical capabilities of human experts. By training models on labeled data representing human judgments and experiences, the system captures subjective human insight in computational form, eliminating the need to repeatedly engage multiple users while preserving the value of human expertise in the analysis process.
Data Source
AI summary
A system, computer readable medium, and method for analyzing digital content of electronic media files includes presenting control media content to a set of control respondents for consumption and presenting test media content to a set of test respondents for consumption. The method includes receiving first responses to a survey related to topics of the control media content from the set of control respondents and second responses to the survey about the test media content from the set of test respondents. The method includes performing feature extraction on the test media content and performing feature extraction on the first responses and the second responses. The feature extraction obtains response features associated with the first responses and the second responses. The method includes training a regression machine learning model with the media content features and the response features.