Digital Asset Review Summarization with ML Safety Filtering
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Solution Overview
Problem
Online reviews often contain ingenuine content, making it difficult for online stores to effectively organize and summarize reviews accurately, as users are incentivized to leave reviews for discounts or through fake accounts, leading to useless review components that distort overall sentiment analysis.
Innovation Solution
Implementing a digital asset manager that utilizes machine learning models to filter, identify sentiment, and generate summaries of online reviews by employing spam filters, language identification, safety checks, sentiment analysis, and informativeness metrics to create structured summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If online reviews are collected from multiple sources, then the quantity of reviews increases, but the quality and reliability of review information deteriorates due to inclusion of ingenuine content
Solution Approach 1:
The system extracts and removes ingenuine review content from the collection of online reviews using machine learning models. The safety metric identifies and filters out fake reviews, incentivized reviews, and gibberish text, retaining only genuine reviews for summary generation. This extraction principle resolves the contradiction by selectively removing harmful elements while preserving the overall quantity of useful reviews.
Solution Approach 2:
The patent introduces an intermediary processing layer between review collection and summary generation. Machine learning models act as intermediaries that evaluate each review's safety, sentiment, and informativeness before inclusion in the final summary. This intermediary layer filters out ingenuine content while allowing genuine reviews to pass through, thus maintaining reliability without sacrificing quantity.
2Loss of information
If all online reviews are included in summaries, then completeness of information is improved, but accuracy of sentiment analysis deteriorates due to noise from ingenuine reviews
Solution Approach 1:
The system applies different quality standards to different reviews individually. Each review is evaluated based on its specific safety metric, sentiment metric, and informativeness metric. Genuine reviews with high quality scores are included in the summary, while ingenuine reviews are excluded. This local quality assessment ensures that the summary maintains high accuracy while preserving completeness of genuine information.
Solution Approach 2:
The patent changes the parameters used to evaluate reviews from simple inclusion criteria to multi-dimensional metrics including safety, sentiment, and informativeness. By transforming the evaluation parameters, the system can distinguish between genuine and ingenuine reviews more effectively, thus improving sentiment analysis accuracy while maintaining information completeness through the use of these refined parameters.
3Reliability
If manual review verification is performed, then the reliability of review quality is improved, but the time and effort required increases significantly
Solution Approach 1:
The system replaces manual mechanical verification of reviews with automated machine learning models. The safety metric, sentiment metric, and informativeness metric are computed automatically by ML models rather than human reviewers. This substitution maintains high reliability in review quality assessment while dramatically reducing the time and effort required, as the automated models process reviews instantaneously without human intervention.
4Measurement precision
If multiple machine learning models are used for each review, then the precision of review evaluation is improved, but the device complexity increases
Solution Approach 1:
The patent employs multiple machine learning models that serve different functions within the review evaluation system. Each model is specialized for a specific metric (safety, sentiment, or informativeness), but collectively they provide comprehensive review evaluation. This multi-functionality approach improves measurement precision by having dedicated models for each aspect while managing complexity through clear functional separation and modular architecture.
Data Source
AI summary
A computer-implemented method for managing information for digital assets is disclosed. The method includes identifying, for a plurality of review associated with a digital asset, respective machine-learning models based on corresponding languages of the plurality of reviews, and removing a given review of the plurality of reviews based on a safety metric to generate a plurality of retained review, where the safety metric is output by a corresponding machine-learning model in response to receiving the given review as input, The method further includes establishing, for the plurality of retained reviews using corresponding machine-learning models, corresponding sentiment metrics and corresponding informativeness metrics, generating a summary for the digital asset based on respective sentiment metrics and respective informativeness metrics, and causing the summary to be displayed within a user interface associated with the digital asset.