Trust Score Calculation via Term Vector Segmentation
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Solution Overview
Problem
Current methods lack a standardized and efficient way to quantify trust perceptions of entities within social media documents, hindering businesses in establishing benchmarks, detecting trust changes over time, and setting improvement goals for marketing and communications.
Innovation Solution
A computer system and method that calculates a composite trust score by building preliminary term vectors indicative of trust factors, refining them into industry-specific vectors, and applying real-time analysis to social media articles using text analytics, which integrates entity extraction, tone analysis, and boost calculations to generate a final trust score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized algorithms are implemented to quantify trust perceptions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The trust quantification system is segmented into distinct functional modules: a trust factor identification module that extracts relevant trust indicators from social media documents, a term vector generation module that creates standardized representations of trust factors, and a score calculation module that computes trust scores. This segmentation enables precise measurement through standardized algorithms while managing system complexity through modular architecture.
Solution Approach 2:
The system transforms unstructured trust perceptions into structured parameters by converting trust factors into standardized term vectors with defined dimensions and weights. This parameter transformation enables precise quantitative measurement of trust while maintaining manageable system complexity through consistent parameter representations.
2Productivity
If real-time analysis is performed on social media documents, then productivity is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying trust factors and pre-generating term vectors from social media documents before final trust score calculation. This preliminary processing enables real-time trust measurement (improved productivity) while optimizing energy usage by preparing data structures in advance rather than performing intensive computations during real-time scoring.
3Measurement precision
If comprehensive trust factor analysis is conducted, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system extracts only the most relevant trust factors from social media documents using identification rules and filters, rather than analyzing all document content comprehensively. This selective extraction maintains measurement precision by focusing on key trust indicators while significantly reducing the time required for trust score calculation.
Data Source
AI summary
A computer system measures trust of an entity in electronic documents from electronic media sources is described. A communication network is linked to one or more of the sources. A computer server is in communication with the communication network and is configured to receive electronic documents via the communication network. The computer server having a memory and a processor accessing a database. The memory includes processor executable instructions stored in the memory and executable by the processor. The computer executable instructions comprise preliminary term vector instructions, calculating instructions for determining the preliminary term vectors in the received electronic documents, and refined term vector instructions for defining a plurality of industry-specific term vectors.