Automated Comparative Post Extraction from User-Generated Content
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Solution Overview
Problem
User-generated content (UGC) from online platforms, such as social media and forums, is difficult to search and analyze for product comparisons due to its broad and freeform nature, making it challenging to extract valuable insights about product features and user sentiments.
Innovation Solution
An AI/ML-powered framework that scrapes and analyzes product reviews from various online forums, employing preprocessing, keyword filtering, lexico-syntactic pattern matching, post classification, and information extraction to identify comparative posts and extract relevant information about product features, pros, cons, and user sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user-generated content from online platforms is collected for product analysis, then the quantity of available data increases, but the difficulty of searching and analyzing the content increases due to its broad and freeform nature
Solution Approach 1:
The patent segments the analysis process into distinct stages: collecting UGC data, preprocessing to identify comparative posts, extracting product information, and generating insights. This segmentation transforms the overwhelming task of analyzing all UGC into manageable steps, where each stage processes only relevant data with specific objectives.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw UGC data and final analysis results. This layer includes preprocessing steps that identify comparative posts and extract relevant information, acting as a mediator that transforms unstructured freeform content into structured data suitable for analysis.
2Loss of information
If all user-generated content is processed for analysis, then comprehensive coverage is achieved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of UGC data that contains product comparison information, rather than processing all available content. By identifying and extracting comparative posts through preprocessing and pattern recognition, the system achieves comprehensive coverage of relevant data while minimizing processing of irrelevant content.
Solution Approach 2:
The patent applies partial action by focusing processing efforts only on posts that contain product comparison indicators. Rather than exhaustively analyzing every piece of UGC, the system performs targeted analysis on identified comparative posts, achieving sufficient comprehensiveness for product analysis without excessive resource consumption.
3Measurement precision
If manual analysis of product reviews is performed, then detailed insights can be obtained, but the productivity and scalability of the analysis process decreases
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational processing. Machine learning models and natural language processing algorithms automatically extract product information, compare features, and generate insights from UGC data, maintaining detailed analysis capabilities while dramatically increasing productivity and scalability.
Solution Approach 2:
The system enables self-service analysis where the computational model automatically processes UGC data, identifies comparative posts, extracts product information, and generates analysis results without requiring manual intervention at each step. This automation maintains analytical depth while enabling high-volume processing.
Data Source
AI summary
In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving a plurality of posts collected from one or more online forums, social media platforms, or other websites and filtering the plurality of posts, wherein the filtering identifies one or more posts which discuss products which are of interest for a product analysis. The method also includes, by the computing device, classifying, using a machine learning (ML) model, individual posts of the filtered posts as a comparative post or a noncomparative post, wherein the posts classified as comparative posts are used for the product analysis, and extracting information from the comparative posts, wherein the information is to be used for the product analysis. The method further includes storing the extracted information within a data repository.