Feedback Reputation Visualization via Text Extraction
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Solution Overview
Problem
Existing feedback systems for online transactions lack specificity, making it difficult for potential purchasers to differentiate between sellers and understand the qualities that contribute to a seller's reputation, as they often rely on general ratings that do not provide detailed information about the service or goods.
Innovation Solution
A system and method that extracts representative textual phrases or tags from feedback text, visualizes reputation ratings using emoticons, and highlights frequently occurring phrases to provide detailed insights into a seller's strengths and weaknesses, allowing users to filter feedback by category and view detailed information about specific phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general ratings are used to evaluate sellers, then the feedback system is simple and easy to understand, but the feedback lacks specificity and cannot differentiate between sellers' unique qualities
Solution Approach 1:
The patent segments general feedback into specific categorical dimensions (e.g., communication, shipping, item description). Each category is evaluated separately with specific phrases extracted from feedback text, allowing potential purchasers to understand particular strengths and weaknesses of sellers without overwhelming complexity.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically extracts representative phrases from feedback text and maps them to specific categories. This intermediary layer transforms unstructured general feedback into structured specific information without requiring direct user analysis of raw feedback.
2Loss of information
If detailed feedback information is provided, then purchasers can make better-informed decisions, but the feedback becomes harder to process and understand
Solution Approach 1:
The patent applies local quality by providing different levels of detail in different parts of the feedback interface. Summary sections provide high-level categorical ratings for quick understanding, while detailed sections offer specific extracted phrases and examples for purchasers who need more information. Each section serves its specific purpose without overwhelming the user.
Solution Approach 2:
The system adds a dimensional organization to feedback by categorizing information along multiple dimensions (communication quality, shipping efficiency, item accuracy). This dimensional structure allows purchasers to quickly scan relevant categories rather than processing a single undifferentiated mass of text.
3Measurement precision
If representative phrases are extracted and visualized, then seller qualities are highlighted effectively, but the system complexity increases
Solution Approach 1:
The system employs automated text analysis algorithms that extract representative phrases without human intervention. The algorithms automatically identify key terms, categorize feedback, and generate visual representations of seller reputations, eliminating the need for manual analysis while maintaining high precision in reputation measurement.
Data Source
AI summary
In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.