Ontology-Based Text Classification for User Feedback Analysis
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Solution Overview
Problem
Existing systems for analyzing user feedback are inefficient due to manual evaluation and inability to process unknown terms or infer feedback granularity, limiting their usefulness in identifying software or hardware issues.
Innovation Solution
A system and method for generating and updating a knowledge base that uses a dictionary associated with classes of an ontology, where text segments are classified using a classifier to determine probabilities and multi-dimensional information criteria, enabling automated inference of actionable items from user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of user feedback is used, then administrators can identify bugs and issues, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs self-service by automatically classifying and analyzing user feedback without requiring administrator intervention. The classifier processes feedback text, determines categories, and generates summaries autonomously, allowing the system to serve itself in the analysis task while administrators only need to review results when necessary.
Solution Approach 2:
The patent replaces the mechanical manual evaluation process with an automated computational system. Instead of administrators manually reading and categorizing feedback, a classifier using machine learning algorithms processes the text automatically, substituting human mechanical analysis with computational processing that is both faster and scalable.
2Adaptability or versatility
If existing feedback analysis systems are used, then some processing capability is provided, but they cannot correctly process unknown terms or recently-released products
Solution Approach 1:
The system performs preliminary action by pre-processing the feedback text to extract entities, terms, and concepts before classification. This preliminary extraction and normalization of terms allows the classifier to handle unknown or newly released product names more effectively by focusing on the structural and contextual patterns rather than relying solely on pre-trained knowledge of specific terms.
Solution Approach 2:
The patent applies parameter changes by adjusting the classification model's parameters and thresholds based on the specific characteristics of the feedback data. The system can adapt its sensitivity and decision boundaries to accommodate new terms and products, changing the operational parameters of the classifier to maintain accuracy when encountering previously unseen terminology.
3Productivity
If automated classification is implemented, then processing speed increases, but the system may lack accuracy in inferring feedback granularity
Solution Approach 1:
The system applies segmentation by dividing the feedback analysis into distinct stages: text preprocessing, entity extraction, classification, and granularity inference. Each stage handles specific aspects of the analysis independently, allowing the system to process feedback quickly while maintaining accuracy in granularity inference through specialized processing at each segment rather than attempting to do everything in a single step.
Data Source
AI summary
A knowledge base can include a dictionary associated with classes of a model, e.g., an ontology. A text segment that is not found in the dictionary can be received. Feature(s) can be determined for the text segment and, based partly on providing the feature(s) to a classifier, a set of values can be determined. The distribution can include values respectively corresponding to the classes. One of the values can be greater than a predetermined threshold. That value can correspond to a class. An indication identifying the class can be presented via a user interface having functionality to provide input that the text segment is associated with the class, is not associated with the class, or is associated with another class. Based at least partly on adding a new class to the ontology, a precedence table indicating priorities between motifs defining relationships between classes of the ontology can be updated.