Viewer Sentiment Predictor for Digital Media Channels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advertisers and brand promoters face challenges in selecting digital media channels that invoke positive viewer sentiments and avoid emotional controversy, as existing methods for monitoring user-uploaded content are inefficient due to the exponential growth of web-based content, requiring increased manpower or processing power for effective moderation.
Innovation Solution
The development of systems and methods that utilize channel metadata and machine analysis to predict viewer sentiments by assessing digital content items, including text, visual, and audio analysis engines, to determine a channel risk metric, allowing for intelligent selection of promotional channels and content evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content moderation is used to monitor digital media channels, then viewer sentiment safety can be assessed, but the system requires increased manpower and becomes inefficient due to exponential content growth
Solution Approach 1:
The patent replaces manual content moderation (mechanical human review) with automated text analysis engines that use natural language processing, machine learning, and sentiment analysis algorithms to evaluate digital media content, channel descriptions, and user comments, thereby maintaining measurement precision while dramatically improving productivity
Solution Approach 2:
The system enables channels to self-evaluate their sentiment risk profiles by automatically analyzing their own content and metadata through the deployed engines, allowing promoters to independently assess channel safety without requiring manual review resources
2Reliability
If extensive content monitoring is implemented to ensure promoter safety, then negative sentiment risks can be identified, but the complexity and processing requirements increase exponentially
Solution Approach 1:
The patent segments the content analysis system into specialized engines: text analysis engines for sentiment detection, visual analysis engines for image/video content, and audio analysis engines for speech recognition, allowing each component to handle specific aspects of content moderation independently and reducing overall system complexity
Solution Approach 2:
The system performs preliminary analysis of channel metadata, descriptions, and historical content before promotional content is deployed, pre-identifying potential sentiment risks and allowing promoters to make informed decisions upfront rather than requiring continuous complex monitoring
3Object-affected harmful factors
If traditional content moderation methods are used, then basic safety checks can be performed, but the system cannot keep pace with exponential content growth
Solution Approach 1:
The patent replaces traditional manual content moderation with automated text analysis engines utilizing natural language processing and machine learning algorithms that can evaluate sentiment, toxicity, and appropriateness of digital media content at scale, maintaining harmful factor detection while achieving exponential processing speed improvements
Solution Approach 2:
The system dynamically adjusts analysis parameters and thresholds based on content type, channel history, and promotional context, allowing flexible optimization of detection sensitivity and processing speed to handle varying content volumes and risk levels
Data Source
AI summary
The herein disclosed technology provides methods and systems for intelligently predicting viewer sentiments invoked by a collection of digital content (e.g., a web-based digital channel) based on an assessment of channel metadata, such as channel metadata defining an association between the channel and one or more other channels; channel history data for the channel; and demographic information about the channel.