Theme-Classifying ML Model for Unstructured Text Analysis
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Solution Overview
Problem
Conventional user experience systems are inaccurate, inefficient, and inflexible in gathering and analyzing user feedback, as they often categorize user experiences broadly, miss specific aspects of user satisfaction, and are limited to known issues without identifying new problems.
Innovation Solution
The system utilizes a theme-classifying machine-learning model to generate theme classifications from unstructured text, associating these classifications with experience scores, and performing actions based on the analysis, thereby improving accuracy, efficiency, and flexibility in understanding user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use surveys with defined questions to gather user experience information, then they can receive structured feedback, but they lose valuable user insights relating to discrete portions of the online transaction experience
Solution Approach 1:
The patent segments user experience feedback into discrete themes and aspects using machine learning classification. Instead of treating user feedback as a single broad category, the system divides it into specific thematic segments (e.g., billing, customer service, product features) allowing precise analysis of discrete portions of the user experience while maintaining overall context.
Solution Approach 2:
The patent replaces the mechanical survey question system with an automated machine learning-based text analysis system. Natural language processing algorithms automatically classify and categorize unstructured user feedback, eliminating the need for predefined survey questions while extracting specific aspect information that would be lost in conventional approaches.
2Loss of information
If conventional systems direct users to successive graphical user interfaces to answer multiple questions, then they can gather detailed information, but they require tremendous time and computing resources to complete
Solution Approach 1:
The patent merges multiple survey questions and feedback collection steps into a single unified process. By analyzing unstructured text feedback in one pass using machine learning, the system simultaneously extracts multiple information elements that would otherwise require separate questions, dramatically reducing completion time while maintaining information completeness.
Solution Approach 2:
The machine learning system performs self-service analysis of user feedback without requiring users to navigate multiple interfaces or answer structured questions. The automated classification process independently extracts and categorizes information from unstructured text, eliminating the time-consuming manual survey completion process while gathering comprehensive feedback.
3Adaptability or versatility
If conventional systems only ask questions about known issues, then they can gather feedback on existing problems, but they cannot identify additional issues or problems without expending additional time and computing resources
Solution Approach 1:
The patent implements a dynamic feedback analysis system using machine learning that can adapt to identify both known and emerging issues. The classification model continuously learns from new data patterns, automatically adjusting to recognize novel problems without requiring predefined question structures, thereby maintaining flexibility while minimizing additional resource expenditure.
Solution Approach 2:
The system performs preliminary analysis of unstructured feedback text to identify potential new issues before they are formally categorized. By pre-processing and classifying all user feedback through machine learning, the system proactively detects emerging patterns and novel problems that would otherwise require additional survey iterations to discover.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a theme classification from unstructured text. In particular, in one or more embodiments, the disclosed systems receive experience data comprising an experience score and unstructured text. The disclosed systems can utilize a theme-classifying machine-learning model to generate a theme classification from the unstructured text and associate the theme classification to the experience score. Moreover, in some embodiments, the disclosed systems can determine and take actions based on the theme classification.