Content Insight System for Granular Sentiment Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Product manufacturers lack insight into the specific factors influencing user ratings, as existing rating systems provide only a single overall rating without detailing the features liked or disliked by users, especially from non-structured reviews on social media platforms.
Innovation Solution
An insight system that identifies brands, subjects, attributes, and sentiments in user reviews across various data sources, pairing these elements to generate granular metrics on user sentiment for product features, providing more accurate and detailed insights into user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a single overall rating system is used, then the rating system is simple and easy to operate, but the manufacturer lacks insight into the specific factors influencing the rating
Solution Approach 1:
The patent segments the overall rating into multiple attribute-level ratings by extracting and analyzing specific features mentioned in user reviews. The system divides the rating feedback into distinct attributes (e.g., quality, durability, design) so that manufacturers can understand which specific features drive overall satisfaction, resolving the contradiction between simplicity and information depth.
Solution Approach 2:
The patent introduces an intermediary processing layer (natural language processing system) that mediates between the simple overall rating input and the detailed attribute analysis output. This intermediary extracts, processes, and transforms the rating information into actionable insights without requiring users to directly provide detailed feedback, thus maintaining ease of operation while gaining deep insights.
2Loss of information
If text reviews are analyzed to provide detailed feature insights, then the manufacturer gains detailed insight into liked and disliked features, but the system complexity increases
Solution Approach 1:
The patent replaces manual analysis of text reviews with automated natural language processing technology. Instead of requiring complex manual analysis systems, the invention uses AI-based NLP models that automatically extract attributes and sentiments from text, significantly reducing system complexity while providing detailed feature insights.
Solution Approach 2:
The patent transforms the analysis approach by changing the parameter of review processing from raw text to structured attribute-sentiment pairs. The system converts unstructured text feedback into quantifiable attribute ratings and sentiment scores, making the complex analysis process more manageable and the output more actionable for manufacturers.
3Loss of information
If only overall ratings are collected, then the data collection process is simple, but the manufacturer cannot identify which specific features contribute to the rating
Solution Approach 1:
The patent applies preliminary action by pre-defining the attribute framework and processing templates before analyzing reviews. The system prepares the analytical structure in advance, allowing rapid extraction of feature contributions without time-consuming on-the-fly analysis, thus reducing data processing time while maintaining comprehensive insights.
Data Source
AI summary
An insight system identifies brands, subjects, attributes, and the sentiment conveyed for those attributes. The insight system pairs the attributes with the subjects and brands and generates metrics based on the sentiments associated with the attributes. The insight system may parse product webpages linked to the content for brand identifiers and associate the brand identifiers with the brands. The insight system provides more granular insight into user sentiment for different features associated with the brands.