Subjective Wellbeing Analytics Score Generation System
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Solution Overview
Problem
Civic and business leaders in cities struggle to understand citizens' thoughts and beliefs on current issues due to the vast and unutilized data from social media, which is not efficiently harnessed for understanding subjective wellbeing.
Innovation Solution
A system that collects and processes social media data to generate a subjective wellbeing analytics score by performing natural language processing, assigning documents to wellbeing dimensions, and determining scores for Affect, Relationships, Focus, Purpose, Fulfillment, and Personal Health, providing a consistent measure of quality of life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social media data is collected and processed to generate wellbeing analytics scores, then understanding of citizens' thoughts and beliefs is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex task of analyzing social media data into multiple processing stages: data collection from multiple sources, natural language processing to extract sentiments and topics, dimension-specific analysis for six wellbeing dimensions, and score aggregation. This segmentation makes the system more manageable and scalable while comprehensively capturing citizens' thoughts.
Solution Approach 2:
The patent introduces intermediary components including natural language processing algorithms that translate unstructured social media text into structured sentiments and topics, and dimension-specific analysis modules that act as intermediaries between raw data and final wellbeing scores. These intermediaries simplify the complexity of directly analyzing vast social media data.
2Measurement precision
If natural language processing is performed on social media documents to assign wellbeing dimensions, then measurement precision of subjective wellbeing is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary natural language processing to extract sentiments and topics from social media documents before assigning them to wellbeing dimensions. This preliminary extraction prepares the data in advance, making the subsequent dimension assignment more efficient and accurate while reducing overall processing time.
Solution Approach 2:
The patent applies partial processing by focusing natural language processing efforts on extracting only the specific features (sentiments and topics) relevant to wellbeing dimensions, rather than analyzing all aspects of the social media data. This selective approach maintains measurement precision while reducing computational overhead.
3Adaptability or versatility
If multiple subjective wellbeing dimensions are analyzed separately, then comprehensiveness of quality of life assessment is improved, but system complexity and analysis time increase
Solution Approach 1:
The patent divides the comprehensive wellbeing assessment into six distinct dimensions (Affect, Relationships, Focus, Purpose, Fulfillment, and Personal Health), each analyzed by separate modules. This segmentation allows each dimension to be assessed with specialized criteria while maintaining overall comprehensiveness, and enables modular system architecture that manages complexity.
Solution Approach 2:
The patent creates a universal framework where the same basic processing architecture (data collection, NLP, dimension assignment, score aggregation) serves all six wellbeing dimensions. This multi-functional approach allows comprehensive assessment while reusing code and processes across dimensions, reducing overall system complexity.
4Quantity of substance
If social media data from multiple sources is collected, then quantity and variety of information is improved, but data management and storage requirements increase
Solution Approach 1:
The patent merges data from multiple social media sources into a unified collection process and centralized storage system. By combining data ingestion, storage, and initial processing into integrated components, the system manages the volume and variety of multi-source data more efficiently than separate systems for each source.
Solution Approach 2:
The patent implements a universal data management architecture that handles multiple social media sources through common interfaces and standardized processing pipelines. This multi-functional system can accommodate different data sources without requiring separate management infrastructure for each, reducing overall complexity while maintaining data quantity and variety.
Data Source
AI summary
A system includes at least one processor to perform natural language processing on text from at least one document and assign the at least one document to at least one subjective wellbeing dimension by comparing the text from the at least one document with a subjective wellbeing dimension filter for each subjective wellbeing dimension, insert the at least one document into at least one bin, each bin associated with a particular subjective wellbeing dimension, and analyze each document in each bin associated with the particular subjective wellbeing dimension to determine a score for each subjective wellbeing dimension and an overall score that is based on each score for each subjective wellbeing dimension.