Sentiment Attribute Selection via Visualization Analytics
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Solution Overview
Problem
Enterprises face challenges in analyzing customer feedback due to the large number of attributes in feedback data, making it difficult to select a meaningful subset for sentiment analysis.
Innovation Solution
Visualization analytics techniques are used to systematically select a subset of attributes based on criteria like frequency of occurrence, relative feedback amounts, time density, and application-specific importance, allowing for real-time sentiment analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all attributes in feedback data are analyzed, then comprehensive sentiment analysis is achieved, but analysis time and computational resources increase significantly
Solution Approach 1:
The patent segments the large set of attributes into multiple subsets and distributes them across multiple computing nodes for parallel processing. Each node analyzes a specific subset of attributes independently, then results are aggregated to produce comprehensive sentiment analysis. This segmentation enables parallel execution, significantly reducing overall analysis time while maintaining comprehensive coverage of all attributes.
Solution Approach 2:
The patent implements iterative refinement where sentiment analysis is performed on progressively larger subsets of attributes. The system starts with a core subset of high-impact attributes and iteratively incorporates additional attributes based on feedback and performance metrics. This approach provides actionable insights from critical attributes quickly, then progressively enhances comprehensiveness without requiring all attributes to be analyzed simultaneously.
2Productivity
If a subset of attributes is selected for analysis, then analysis efficiency is improved, but comprehensiveness of sentiment analysis may be reduced
Solution Approach 1:
The patent implements dynamic attribute subset selection where the composition of analyzed attributes changes over time based on feedback data characteristics, business priorities, and performance metrics. The system dynamically adjusts which attributes are included in analysis subsets, allowing it to optimize between efficiency and comprehensiveness based on current needs. This dynamic adaptation ensures that the most relevant attributes are always analyzed while maintaining operational efficiency.
Solution Approach 2:
The system incorporates feedback loops where sentiment analysis results and performance metrics are used to refine future attribute selection. Attributes that demonstrate higher impact on sentiment detection or show greater variability in feedback are prioritized for inclusion in analysis subsets. This feedback-driven selection process ensures that the subset of analyzed attributes maintains high comprehensiveness relative to its size, improving the efficiency-comprehensiveness trade-off.
3Loss of information
If multiple attributes are analyzed simultaneously, then comprehensive insights are obtained, but system complexity and resource requirements increase
Solution Approach 1:
The patent divides the complex task of analyzing multiple attributes simultaneously into segmented parallel tasks distributed across multiple computing nodes. Each node handles a specific subset of attributes independently, reducing the complexity burden on any single system component. The segmentation approach maintains comprehensive insights through aggregation of results from all nodes while keeping individual system components relatively simple and manageable.
Solution Approach 2:
The patent implements a universal sentiment analysis framework that can handle any attribute subset through a common processing pipeline. The system uses standardized interfaces and data structures that work across different attribute types and analysis configurations. This multi-functionality allows the same system infrastructure to comprehensively analyze diverse attribute sets without requiring separate specialized systems for each attribute combination, thereby reducing overall system complexity.
Data Source
AI summary
Data records containing user feedback regarding at least one offering are received. From among candidate attributes in the received data records, a subset of attributes that relate to user sentiment regarding the at least one offering is selected, where the selecting is according to selection criteria including frequency of occurrence of the candidate attributes, relative amounts of negative and positive feedback associated with the candidate attributes, and time density of feedback associated with the candidate attributes. A visualization of at least one sentiment characteristic of the selected subset of attributes that relate to user sentiment is presented for display.