NLP Feedback Analysis via Temporal Verb Tagging
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Solution Overview
Problem
In continuous delivery environments, gathering and analyzing user feedback is time-consuming and expensive, especially when products/services rapidly change with new feature releases, making it challenging to gauge user reactions and calibrate offerings effectively.
Innovation Solution
A method using natural language processing (NLP) to identify verb-related tags in user feedback, classify it into temporal classes (past, present, and future), and analyze sentiment and topics to understand user perceptions across different versions of a product/service, enabling semi-automated feedback analysis and longitudinal insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feedback analysis is used to gauge user reactions and determine potential new features, then comprehensive insights can be obtained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical feedback analysis with an automated NLP-based system. The NLP component automatically processes user feedback data, identifies verb-related tags, classifies feedback into temporal classes, and determines user perceptions without human intervention, thereby eliminating the time-consuming and expensive manual analysis process while maintaining comprehensive insights
Solution Approach 2:
The feedback analysis system performs self-service by automatically gathering, processing, and analyzing user feedback data through the NLP component. The system independently identifies temporal classes, analyzes sentiment, and generates user perception insights without requiring external manual analysis, enabling continuous automated calibration of product offerings
2Loss of information
If comprehensive feedback analysis is performed on all user feedback data, then complete user perception insights are obtained, but the complexity and cost of the analysis process increases
Solution Approach 1:
The patent extracts only the essential temporal information from comprehensive feedback data by identifying verb-related tags and classifying feedback into temporal classes (past, present, future). This extraction approach obtains complete user perception insights regarding temporal preferences while avoiding the complexity of analyzing every detail of the feedback data
Solution Approach 2:
The feedback analysis process is segmented into distinct automated steps: identifying verb-related tags, classifying into temporal classes, analyzing sentiment, and determining user perceptions. This segmentation maintains information completeness while organizing the complex analysis process into manageable automated components
3Adaptability or versatility
If rapid product version changes are implemented to respond to market needs, then product adaptability improves, but the ability to effectively gauge user reactions diminishes
Solution Approach 1:
The patent implements continuous automated feedback analysis that operates continuously without interruption despite rapid product version changes. The NLP component continuously processes user feedback data, maintaining reliable measurement of user reactions across all version transitions by automatically adapting the analysis to the current version context
Data Source
AI summary
A method, computer system, and a computer program product for feedback analysis is provided. The present invention may include, in response to identifying, using a natural language processing (NLP) component, a verb-related tag in a feedback data associated with a current version of a product or service, classifying the feedback data into a temporal class based on the identified verb-related tag. The present invention may also include, analyzing, using the NLP component, the classified feedback data within the temporal class. The present invention may further include, determining, based on the analyzed feedback data, a user perception associated with the current version of the product or service relative to the temporal class of the analyzed feedback data.