Review Analysis System Using Sentiment Metrics for Product Comparison
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Solution Overview
Problem
Current online tools for reviewing retail products lack analysis of review text to provide objective comparisons and contrasts, relying solely on consumer reviews and failing to quantify ratings of product features or provide cross-referencing features for professional versus non-technical reviews.
Innovation Solution
A web-based system utilizing opinion/sentiment analysis algorithms and supervised machine learning to extract and analyze reviews from various sources, generating informative summaries with metrics and cross-referencing features for product features, allowing users to compare products based on professional and consumer reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automated computer systems compute representativeness, coherence, liveliness, and informativity of composite reviews, then the analysis of review text is enhanced, but the system does not compare and contrast different products from the same class via objective statistical analysis
Solution Approach 1:
The system segments the review analysis process into distinct components: computing representativeness, coherence, liveliness, and informativity of composite reviews as separate analytical functions. This segmentation allows each aspect to be independently optimized while maintaining overall system coherence.
Solution Approach 2:
The system is designed to perform multiple functions: analyzing composite review quality metrics (representativeness, coherence, liveliness, informativity) and comparing/contrasting different products from the same class through objective statistical analysis. This multi-functionality resolves the limitation of prior systems that performed only one type of analysis.
2Reliability
If the system provides separate displays of reviews by professional industry reviewers versus non-technical user reviewers, then the reliability of product evaluation is improved, but the device complexity increases
Solution Approach 1:
The system segments reviews into distinct categories: professional industry reviewer reviews and non-technical user reviewer reviews. This segmentation enables separate display and analysis of each reviewer type, allowing users to evaluate product reliability from different perspectives independently.
Solution Approach 2:
The system introduces an intermediary classification layer that identifies and categorizes reviews by reviewer type (professional vs. non-technical). This intermediary function enables the system to provide separate displays of different review types without requiring complex manual sorting by users.
3Adaptability or versatility
If the system implements cross-referencing features to display other products rated by reviewers, then the ability to comparison shop is improved, but the system complexity increases
Solution Approach 1:
The system introduces cross-referencing as an intermediary function that connects product reviews with other products rated by the same reviewers. This mediator function enables comparison shopping by automatically identifying and displaying related products without requiring users to manually search across multiple product pages.
Solution Approach 2:
The system adds a new dimension to product evaluation by incorporating cross-referencing data that shows how the same reviewers rated other products. This additional dimension enables users to compare products based on reviewer consistency and reliability across multiple products, transforming the evaluation from a single-product to a multi-product perspective.
4Measurement precision
If the system quantifies ratings of product features using statistical analysis, then the objectivity of product comparison is improved, but the processing complexity increases
Solution Approach 1:
The system replaces manual, subjective product feature rating with automated statistical analysis. By using computer-implemented statistical methods to quantify ratings, the system objectively measures product features without requiring human intervention in the measurement process, thereby improving precision while managing complexity through automation.
Data Source
AI summary
A system, method, and computer program product for researching online reviews to assess the performance and functionality of digital media consumer products bought online or not (e.g. eBooks, movies, TV shows, music, DVD's, etc.). The system extracts reviews from multiple online sources, including online “stores”, professional articles, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each product's reviews, wherein each summary includes a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. performance, price, etc.). Additionally, the user may view a separate review detail page per product that provides further summaries, such as a short list of other products that the same reviewer gave a very positive review for the features. The user is then able to purchase the product via a link.