Polarized Sentiment Analysis for Retail Product Insights
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Solution Overview
Problem
Current rating and review systems provide insufficient analysis of user-generated content, leaving retailers with limited insight into customer sentiment and failing to leverage customer bases effectively for product sales.
Innovation Solution
A content intelligence system that analyzes user-generated content using a product polarization module to identify polarized products and their underlying sentiment dimensions, calculating average review ratings, segment distances, and weighted variation scores, and employing Analysis of Variance to determine polarization scores, thereby providing actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current rating and review systems are used to collect user-generated content, then customer sentiment data is gathered, but insufficient analysis is provided leaving retailers with limited insight
Solution Approach 1:
The patent segments customer sentiment analysis into multiple dimensions (e.g., product features, customer demographics, usage scenarios) and uses segmentation to break down the complex analysis task into manageable components, allowing retailers to gain insights without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between raw user-generated content and retailer decision-making, using natural language processing and sentiment analysis algorithms to translate unstructured data into actionable insights, thereby reducing the complexity burden on retailers
2Loss of information
If comprehensive analysis of user-generated content is performed to understand customer sentiment, then insight into why customers feel a certain way is gained, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and categorizing user-generated content as it is collected, organizing data by dimensions and themes before analysis is requested, which reduces processing time when comprehensive sentiment analysis is needed
Solution Approach 2:
The patent changes parameters by adjusting the depth and scope of analysis based on retailer needs, allowing for flexible analysis that can range from quick overview metrics to comprehensive dimensional analysis, thereby balancing insight quality with processing time
3Productivity
If retailers leverage customer base effectively using detailed sentiment analysis, then product sales can be improved, but the complexity of implementing such analysis systems increases
Solution Approach 1:
The patent enables self-service by providing automated sentiment analysis that retailers can implement without requiring extensive in-house analytical expertise, using pre-configured analysis frameworks and user-friendly interfaces that reduce implementation complexity while improving sales conversion through better customer insights
Data Source
AI summary
An overall average review rating for a product may be determined, based on user ratings that are associated with opinions of a product, within a dimension corresponding to a user trait. A segment variation score for each of a plurality of segments of the dimension may be determined. Each segment may correspond to one or more values of the user trait corresponding to the dimension. A total variation score may be determined for the dimension based on the segment variation scores determined for each of the plurality of segments of the dimension. The total variation score for the dimension may be compared to a polarization threshold to determine whether the dimension is polarized. Generated information may identify the product as having sentiment that is polarized.