Automated Extraction Tool for Social Content Tagging
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Solution Overview
Problem
Current social media marketing tools require manual tagging of social content, which is time-consuming and lacks scalability, preventing accurate reporting and insight into the performance of social campaigns.
Innovation Solution
An automated system that uses machine-readable medium instructions to analyze social content with automation logic parameters, extract metadata, and create tags using tag conversion rules, enabling true automatic tagging of social content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used for social content, then tagging accuracy can be maintained, but time consumption and lack of scalability increase
Solution Approach 1:
The patent replaces the manual mechanical tagging process with an automated computer-based system that uses natural language processing and machine learning algorithms to extract metadata and generate tags, eliminating the need for manual human intervention while maintaining tagging accuracy
Solution Approach 2:
The system enables self-service automatic tagging by allowing the social content management system to automatically analyze content, extract relevant metadata, and generate tags without requiring user intervention, thus resolving the contradiction between accuracy and time consumption
2Productivity
If static tag lists are used for automatic tagging, then tagging speed improves, but adaptability and scalability deteriorate
Solution Approach 1:
The patent implements dynamic tag generation where the system automatically adapts to different content types and contexts by using natural language processing to understand content semantics and generate contextually relevant tags, rather than relying on fixed static tag lists
Solution Approach 2:
The system changes the parameter of tag generation from static predefined lists to dynamic algorithmic generation based on content analysis, allowing the tagging system to scale and adapt to various content types and business requirements automatically
3Extent of automation
If automated keyword analysis is used for tagging, then automation level increases, but tagging precision and relevance decrease
Solution Approach 1:
The patent replaces simple keyword analysis with advanced natural language processing and machine learning systems that can understand context, semantics, and relationships in content, thereby maintaining high tagging precision while achieving full automation
Solution Approach 2:
The system introduces intermediary components including natural language processing modules and machine learning models that act as mediators between raw content and generated tags, enhancing the precision of automated tagging by adding layers of semantic understanding
Data Source
AI summary
The present invention relates to novel methods, tools and systems that provide for true automatic tagging of social content that overcome the deficiencies of existing techniques, and their requirement of static tag creation. In particular, the present invention relates to automated extraction tools and their use in creating tags through automated analysis of social media content, and further using the created tags in systems to associate the tag with the original content, e.g., based on user settings.