Content Delivery System with Sentiment Analysis for Emotional Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content delivery systems fail to effectively manage secondary content in relation to primary content, leading to sentiment misalignment that can cause discomfort or aversion in users, such as when a romantic movie is paired with a humorous advertisement.
Innovation Solution
A system and method for analyzing primary and secondary content to determine associated emotions or sentiments, allowing for the selection and output of secondary content based on similarity or difference with the primary content, ensuring emotional alignment or transition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If secondary content is delivered independently of primary content analysis, then content delivery simplicity is maintained, but user experience deteriorates due to sentiment misalignment
Solution Approach 1:
The system performs preliminary analysis of primary content to determine emotions/sentiments before delivering secondary content. This advance preparation enables the system to select appropriate secondary content that aligns with the primary content's emotional tone, preventing sentiment misalignment while maintaining simple delivery operations.
Solution Approach 2:
The system incorporates feedback loops where primary content analysis results are used to guide secondary content selection. This feedback mechanism ensures that secondary content is chosen based on the emotional characteristics of primary content, improving user experience while keeping the delivery process straightforward.
2Reliability
If emotional analysis of content is performed, then user experience is improved through better content alignment, but system complexity increases
Solution Approach 1:
The system introduces an intermediary content analysis module that sits between content delivery components. This intermediary layer handles the emotional analysis and content matching functions, isolating complexity from the main delivery system while improving user experience through better content alignment.
Solution Approach 2:
The content analysis system is designed as a universal component that can analyze various types of content (video, audio, text) and provide emotional insights for multiple delivery scenarios. This multi-functionality reduces overall system complexity by consolidating analysis capabilities into a single reusable module.
3Stability of the object's composition
If secondary content is selected based on emotion similarity, then content coherence is improved, but content diversity decreases
Solution Approach 1:
The system applies local quality by matching secondary content emotions to specific segments or aspects of primary content rather than requiring overall similarity. This allows the system to maintain coherence in relevant areas while introducing diversity in other areas, balancing content alignment with variety.
Solution Approach 2:
The content selection system dynamically adjusts the balance between similarity and diversity based on real-time analysis of primary content emotions. The system can shift between prioritizing coherence and prioritizing diversity depending on the current content context, enabling flexible adaptation to different viewing situations.
Data Source
AI summary
Reaction information and intensity information associated with primary content and secondary content may be determined. Secondary content may be selected based on the reaction information and the intensity information.