Online Information Analysis System for Market Sentiment Tracking
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Solution Overview
Problem
Traditional methods for discovering public opinions about brands, companies, or products are time-consuming and expensive, relying on polls and market surveys, while the vast amount of publicly available online information is underutilized for market perception analysis.
Innovation Solution
A system comprising a collection engine and an analysis engine that accumulates and analyzes online documents to identify changes in public discussion over time, using machine learning techniques to categorize and visualize trends, and alert users to novel topics, facilitating the tracking of market perceptions and customer sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods like polls and market surveys are used to discover public opinions, then measurement precision of public sentiment is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent replaces traditional mechanical survey methods (polls, interviews, focus groups) with an automated electronic system that uses web crawlers, natural language processing, and machine learning algorithms to analyze public sentiment from online sources, thereby eliminating time-consuming manual data collection while maintaining measurement precision
Solution Approach 2:
The system creates a digital copy of public opinion by scraping and analyzing text from web sources, news articles, and social media, producing a replicated representation of public sentiment that can be analyzed without conducting actual surveys or polls
2Reliability
If traditional polls and market surveys are used, then reliability of public opinion data is improved, but productivity of opinion monitoring decreases
Solution Approach 1:
The system implements continuous automated monitoring of online sources, continuously scraping, processing, and analyzing public sentiment data in real-time, eliminating the periodic interruptions inherent in traditional survey methods and maintaining both reliability through consistent data collection and high productivity through uninterrupted operation
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and analyzing public opinion data without requiring human intervention for each data collection cycle, using automated crawlers to fetch data, machine learning models to analyze sentiment, and algorithms to generate insights, thereby achieving both reliability through systematic processing and high productivity through automation
3Measurement precision
If human experts manually analyze online documents for sentiment, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The patent replaces manual human expert analysis with automated machine learning models and natural language processing algorithms that can process vast amounts of text data systematically, maintaining measurement precision through trained models while reducing device complexity by eliminating the need for human expert intervention in each analysis case
Solution Approach 2:
The system uses parameter changes in the form of machine learning model parameters and thresholds that can be adjusted to optimize sentiment analysis accuracy, allowing the system to adapt to different contexts and maintain high measurement precision without increasing operational complexity, as the models automatically process data based on configured parameters
4Quantity of substance
If the system analyzes all available online documents, then quantity of information processed increases, but loss of time and computational resources worsen
Solution Approach 1:
The system extracts only the relevant information from vast online sources by using targeted web crawlers that follow predefined rules and filters that identify sentiment-bearing content, thereby processing a manageable quantity of high-value information rather than attempting to analyze all available documents, reducing processing time while maintaining comprehensive coverage of relevant public opinion
Solution Approach 2:
The system applies partial action by focusing analysis on specific document sections that contain sentiment information (such as review bodies, article conclusions, or social media posts) rather than processing entire documents, and uses filtering to exclude irrelevant content, thereby achieving efficient processing of the essential information volume without wasting time on extraneous material
Data Source
AI summary
The present disclosure includes systems and techniques relating to online information analysis. In general, in one implementation, a system includes a collection engine configured to accumulate document information retrieved from publicly accessible network resources according to predefined subjects, and an analysis engine configured to analyze the accumulated document information to identify change over a time period in general discussion of a topic within a selected subject of the predefined subjects, the analysis engine further configured to normalize the identified change over the time period based on change in a total number of documents found for the selected subject during the time period.