Data Set Labeling Using Breadth Scores for Accurate Coding
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Solution Overview
Problem
Existing coding schemes often fail to accurately represent data sets due to broad or narrow labels, and overlapping concepts, leading to inefficiencies in qualitative analysis and text mining.
Innovation Solution
A system utilizing natural language processing to analyze and update coding schemes by calculating conceptual breadth, density, and overlap, suggesting changes or generating new schemes to improve accuracy and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If researchers manually refine coding schemes throughout the coding process, then the coding scheme can be adjusted to better represent the data set, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary analysis of the data set before coding begins to automatically generate an initial coding scheme. This preliminary action includes analyzing data characteristics, identifying key themes, and creating initial labels, thereby eliminating the need for manual refinement during the coding process and saving significant time while maintaining accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of coding scheme refinement with an automated computational system. The system uses algorithms to analyze the data set, generate coding schemes, and refine labels automatically, substituting human researchers' manual work with machine-based processing that is both faster and more consistent.
2Adaptability or versatility
If a coding scheme uses broad labels to cover more topics, then fewer labels are needed, but the precision of theme identification decreases
Solution Approach 1:
The system applies local quality by creating labels with different levels of specificity tailored to the actual distribution and characteristics of themes in the data set. Rather than using uniformly broad or narrow labels, the system analyzes where precise differentiation is needed and where broader categorization suffices, optimizing each label's granularity to its local requirements.
Solution Approach 2:
The system dynamically adjusts the granularity parameter of labels based on data analysis results. It changes the specificity level of labels according to the actual theme distribution in the data set, transforming fixed broad labels into adaptive labels that achieve both wide coverage and precise identification where needed.
3Measurement precision
If researchers curate coding schemes to comprehensively represent a data set, then the coding scheme becomes more accurate, but the complexity of scheme development increases
Solution Approach 1:
The system performs self-service by automatically analyzing the data set and generating coding schemes without requiring extensive manual curation by researchers. The system independently identifies themes, creates labels, and structures the coding scheme, thereby reducing the complexity of scheme development while maintaining comprehensive representation of the data.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated coding scheme is evaluated against the data set, and adjustments are automatically made based on performance metrics. This feedback loop ensures comprehensive representation while simplifying development, as the system iteratively refines the scheme based on objective criteria rather than manual trial and error.
Data Source
AI summary
A method for labeling a data set by a coding model includes generating multiple sets of related initial labels based on processing a data set with a group of initial labels. The method also includes determining a quantity of occurrences, within the data set, of each one of the group of initial labels and each related initial label of the multiple sets of related initial labels. The method further includes determining, for each initial label of the group of initial labels, a breadth score based on the number of occurrences of each related initial label. The method still further includes updating one or more of the group of initial labels based on respective breadth scores satisfying a label updating condition. The method also includes labeling the data set based on the group of initial labels and the multiple sets of related initial labels.