Consumer Review Analysis Engine Using NLP Sentiment Extraction
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Solution Overview
Problem
Conventional techniques for processing and analyzing consumer reviews are inefficient, as they require manual evaluation, lack consistency, and fail to normalize qualitative and quantitative reviews, making it difficult for consumers to make informed purchase decisions and for businesses to effectively utilize review information.
Innovation Solution
A computer-executable method and system that programmatically analyzes consumer reviews by extracting attribute descriptors and sentiment scores, allowing for consistent and accurate evaluation of commercial entities or objects, using natural language processing and machine learning to identify and categorize sentiments and attributes from large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of consumer reviews is used, then analysis accuracy can be maintained, but processing efficiency and consistency deteriorate
Solution Approach 1:
The patent replaces manual mechanical evaluation of consumer reviews with an automated computer-based processing system. The system uses natural language processing and machine learning algorithms to automatically extract attribute descriptors and sentiment scores from reviews, eliminating the need for manual reading and analysis while maintaining consistent and accurate results across large volumes of data.
Solution Approach 2:
The system enables self-service automated analysis where the computer processing engine independently evaluates consumer reviews without human intervention. The engine automatically identifies attributes, determines sentiment, and generates analysis results, allowing the system to serve itself in processing and analyzing review data at scale.
2Productivity
If automated processing is implemented, then processing efficiency improves, but analysis consistency and accuracy worsen
Solution Approach 1:
The patent transforms unstructured consumer review text into structured data parameters including attribute descriptors and sentiment scores. By converting qualitative review content into quantifiable parameters that can be systematically processed and compared, the system achieves both high processing efficiency and consistent, measurable analysis results across different reviews.
Solution Approach 2:
The system segments consumer reviews into distinct components including attribute descriptors (specific product features mentioned) and sentiment scores (positive/negative/neutral ratings). This segmentation allows the automated system to process each element independently and consistently, improving both efficiency and reliability of the analysis.
3Adaptability or versatility
If qualitative and quantitative reviews are normalized, then data usability improves, but processing complexity increases
Solution Approach 1:
The patent creates a universal processing framework that handles both qualitative text reviews and quantitative star ratings through the same attribute descriptor and sentiment score extraction process. The system normalizes different review types into a common structured format, enabling unified analysis and comparison across diverse data sources while managing complexity through standardized processing pipelines.
Data Source
AI summary
Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically analyzing a consumer review. The method includes programmatically accessing, via a network device, one or more consumer reviews for a commercial entity or a commercial object. The method also includes executing a consumer review processing engine to programmatically identify an attribute descriptor in the one or more consumer reviews, and executing the consumer review processing engine to programmatically generate a sentiment score associated with the one or more consumer reviews. The method further includes storing, on a non-transitory computer-readable storage device, the attribute descriptor and the sentiment score in association with the commercial entity or the commercial object.