Sentiment-Based Social Media Comment Overlay on Image Posts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social media technologies are inadequate in relating comments to non-human entities within image posts, as they rely on facial recognition and image metadata, which are insufficient for understanding the context of natural language comments and associating them with entities like objects or background elements in uncurated social media images.
Innovation Solution
A method using Natural Language Processing (NLP) to analyze comments and determine the entities they refer to, even if not explicitly mentioned, and overlay sentiment-based graphical artifacts on the corresponding entities within the image, considering the entity's presence, sentiment, and comment frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If facial recognition and image metadata are used to relate comments to entities, then the system can identify human faces and basic image information, but it cannot understand the context of natural language comments and associate them with non-human entities like objects or background elements
Solution Approach 1:
The patent introduces Natural Language Processing (NLP) as an intermediary system between image metadata and comment analysis. The NLP engine processes comment text to extract entities and their contexts, bridging the gap between simple image metadata and complex natural language comments. This intermediary enables the system to understand contextual relationships and associate comments with non-human entities that facial recognition and basic metadata cannot identify.
2Measurement precision
If the system analyzes every comment to determine entity context and sentiment, then accurate association is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent implements a selective analysis approach where not every comment undergoes full NLP processing. Instead, the system applies sentiment analysis and entity extraction only to comments that are likely to contain relevant entity references, determined by initial filtering based on comment characteristics. This partial action approach maintains high association accuracy for relevant comments while reducing overall processing time and computational resources.
3Loss of information
If graphical artifacts are overlaid on all entities with sentiment values, then comprehensive visual feedback is provided, but the visual complexity and information overload increase
Solution Approach 1:
The patent applies different visual representation strategies to different entities based on their characteristics and sentiment values. Rather than uniformly overlaying graphical artifacts on all entities, the system selectively applies visual indicators only to entities with significant sentiment associations or those that are most relevant to the comment context. This local quality approach provides comprehensive visual information for important entities while avoiding information overload and visual complexity for less relevant entities.
Data Source
AI summary
By performing Natural Language Processing (NLP) on a comment to a social media post, an entity that is referenced in the comment is extracted. The entity is an object other than a human face that is depicted in an image in the post. The image is analyzed to determine whether the entity is represented in the image. When the entity is represented in the image, a sentiment value of the comment is computed relative to the entity. A value corresponding to the sentiment value is assigned to a characteristic of a graphical artifact. A position is determined relative to an area occupied by the entity in the image. The graphical artifact is caused to be overlaid on the image at the position and with the value of the characteristic.