Sentiment Mapping for Media Content Ad Placement
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Solution Overview
Problem
The integration of traditional and new media in advertising campaigns often leads to 'advertising dissonance' due to conflicting sentiments between the media content and advertisements, reducing user enjoyment and advertisement effectiveness.
Innovation Solution
Creating a 'sentiment map' that evaluates and delimits segments of media content items based on sentiment states, considering video, audio, metadata, and social responses, and personalizes the evaluation based on user preferences to match advertisements with favorable content segments in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If advertisements are placed in traditional media (TV, magazines) to reach broad audiences, then advertising coverage is improved, but advertising dissonance occurs due to conflicting sentiments between media content and advertisements
Solution Approach 1:
The patent segments media content into distinct sentiment states (e.g., happy, sad, exciting, funny) and places advertisements only in segments with matching sentiments. This segmentation allows broad coverage across different content types while ensuring each advertisement appears in sentiment-appropriate contexts, eliminating advertising dissonance.
Solution Approach 2:
The patent applies local quality by assigning different sentiment characteristics to different segments of media content. Each segment is evaluated and tagged with its dominant sentiment, allowing advertisements to be matched with specific local segments rather than being uniformly placed throughout entire media pieces. This ensures sentiment compatibility at the local level where advertisements actually appear.
2Reliability
If sentiment analysis is performed on media content to prevent advertising dissonance, then advertisement placement quality is improved, but system complexity increases due to multiple evaluation factors
Solution Approach 1:
The patent introduces a sentiment analysis intermediary system that acts as a mediator between media content and advertisements. This intermediary automatically evaluates media segments for sentiment states using multiple factors (video, audio, metadata, social responses) and generates sentiment tags that simplify the advertisement placement decision process. The intermediary handles the complexity of multi-factor evaluation, presenting simplified sentiment information to the ad placement system.
Solution Approach 2:
The patent performs preliminary sentiment analysis and segmentation of media content before advertisement placement decisions are made. By pre-evaluating and tagging media segments with their sentiment states, the system prepares the groundwork for efficient ad matching. This preliminary action eliminates the need for complex real-time analysis during ad placement, reducing system complexity while maintaining high placement quality.
3Adaptability or versatility
If real-time sentiment evaluation is performed during media playback, then advertisement relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs sentiment analysis and segments media content in advance, before advertisement placement is needed. This preliminary processing creates a sentiment map of the media content that can be quickly referenced during real-time ad placement. By moving the computationally intensive analysis to a preliminary stage, the system achieves both real-time relevance and efficient processing without excessive time or resource consumption during actual ad delivery.
Data Source
AI summary
A media content item is evaluated for its “sentiment states.” That is, segments of the content item are determined to be, for example, “happy,”“exciting,”“sad,”“funny,” and the like. A “sentiment map” is created that delimits segments of the content item and contains the sentiment-state keywords associated with the segments. Some maps include an amplitude for each assigned sentiment keyword and a confidence value for the segment delimitation and for each keyword. As an exemplary use of the sentiment map, an advertisement broker matches the sentiments of his advertisement offerings with segments of a content item in order to place appropriate advertisements at times when they would be most favorably received. In another example, a recommender system recommends to a user a content item whose sentiment map compares favorably to that of a content item already enjoyed by the user.