User Review Entity Association via Text Segmentation
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Solution Overview
Problem
Existing methods fail to effectively analyze and associate descriptive segments of text from user reviews with relevant entities such as products, product creators, and vendors, limiting the ability to categorize and index user interest and feedback efficiently.
Innovation Solution
A computer-implemented method using natural language processing techniques to classify user reviews, extract descriptive segments of text, and associate them with entities, employing machine learning classifiers and graph engines to index entities based on observed user interest and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to analyze user reviews, then the analysis process is simple, but the ability to effectively associate descriptive segments with entities and categorize user interest is limited
Solution Approach 1:
The patent segments user reviews into distinct portions (e.g., product-related portions, creator-related portions) and extracts descriptive segments from each. This segmentation enables precise association of different text segments with appropriate entities (products, creators, vendors) while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The patent introduces an intermediary system that includes a graph engine and machine learning classifiers. This intermediary processes user reviews, extracts descriptive segments, and associates them with entities, thereby improving association accuracy without requiring direct complex interactions between all system components.
2Productivity
If manual analysis of user reviews is performed, then detailed insights can be obtained, but the processing efficiency and scalability are poor
Solution Approach 1:
The system enables self-service automated analysis of user reviews through machine learning classifiers and graph engines. These components automatically process large volumes of reviews, extract descriptive segments, and associate them with entities, achieving high processing efficiency while preserving detailed information through structured extraction and indexing.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems including natural language processing, machine learning classifiers, and graph engines. This substitution dramatically improves processing efficiency while maintaining information depth through systematic extraction and association of descriptive segments with entities.
3Quantity of substance
If comprehensive text extraction from user reviews is performed, then more entity attributes are captured, but the complexity of classification and indexing increases
Solution Approach 1:
The patent segments user reviews into distinct portions (product-related, creator-related, vendor-related) and applies specific classification rules to each segment. This segmentation enables comprehensive extraction of entity attributes while managing classification complexity through modular, targeted processing of different review portions.
Solution Approach 2:
The patent applies different classification and extraction approaches to different portions of user reviews based on their local characteristics. Product-related portions are classified for product attributes, creator-related portions for creator attributes, and so on. This local quality approach captures comprehensive entity attributes while simplifying the overall classification process through context-specific handling.
Data Source
AI summary
Methods and apparatus are described herein for classifying user reviews or portions thereof as being related to various entities, and for associating extracted descriptive segments of text contained in those user reviews or portions thereof with entities based on the classifications. In various implementations, one or more categories of observed user interest may be identified based on a corpus of user queries. One or more segments of text related to the one or more categories of observed user interest may be detected in one or more user reviews associated with a product. Based on the detecting, the product may be indexed on the one or more categories of observed user interest in a searchable database. In some implementations, the searchable database may be accessible to one or more remote client devices, and may be searchable by the one or more categories of observed user interest to provide search results to be rendered by the one or more remote client devices.