Multi-threaded Text Affinity Analyzer for Granular Sentiment Detection
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Solution Overview
Problem
Existing social media analytics tools fail to accurately detect granular-level differences in sentiment between social media postings, leading to inappropriate intervention strategies that can cost companies money and reduce customer satisfaction.
Innovation Solution
A multi-threaded text affinity analyzer that compares social media communications at a granular level by dividing posts into threads based on sentiment types, allowing for more precise analysis and tailored intervention strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing social media analytics tools are used to analyze postings, then analysis can be performed, but granular-level sentiment differences cannot be accurately detected
Solution Approach 1:
The patent segments social media postings into multiple threads based on sentiment types (positive, negative, neutral). Each thread represents a distinct sentiment category, allowing the system to analyze sentiment differences at a granular level by comparing specific thread pairs rather than treating entire postings as single units. This segmentation enables precise detection of sentiment differences while managing complexity through structured organization.
2Measurement precision
If granular-level text affinity analysis is performed, then accurate sentiment differentiation is achieved, but processing time and computational resources increase
Solution Approach 1:
By dividing postings into sentiment-based threads, the system performs affinity analysis on smaller, focused text segments rather than entire postings. This reduces the computational burden of each comparison while maintaining granular-level precision, as each thread comparison is faster and more targeted than analyzing complete postings.
Solution Approach 2:
The system applies text affinity analysis selectively to specific thread pairs with matching or contrasting sentiment types, rather than performing exhaustive comparisons across all possible posting combinations. This localized approach concentrates computational resources on the most relevant comparisons, improving efficiency while maintaining measurement accuracy.
3Reliability
If traditional analytics tools are used, then cost is reduced, but inappropriate intervention strategies are implemented
Solution Approach 1:
The system segments both the input data (postings into sentiment threads) and the output recommendations (intervention strategies tailored to each thread type). This dual segmentation ensures that intervention strategies are appropriately matched to the specific sentiment context, improving reliability by avoiding one-size-fits-all approaches while maintaining manageable complexity through structured processing.
Data Source
AI summary
A method and system are disclosed for analyzing text affinity among a plurality of social media communications, comprising dividing a first social media communication into first plurality of social media communication threads; dividing a second social media communication into a second plurality of social media communication threads; performing a text affinity analysis operation between respective threads of the first plurality of social media communication threads and the second plurality of social media communication threads; and, determining a level of intervention to perform based upon the text affinity analysis operation.