Web Content Mining System for Sentiment Taxonomy Generation
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Solution Overview
Problem
Current search systems fail to dynamically analyze human trends and sentiments from internet-based information, leading to difficulties in extracting meaningful information, especially as irrelevant data accumulates over time, and are unable to distinguish between relevant and irrelevant content, such as news articles and advertisements.
Innovation Solution
A system that mines user-generated content from the web to create real-time taxonomies by extracting sentiment and non-sentiment phrases, associating them to generate taxonomies, and storing these in a data warehouse for responsive querying, enabling the determination of relationships and trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search systems provide access to all available internet information, then information availability is improved, but information quality and relevance deteriorate due to large amounts of unwanted information
Solution Approach 1:
The patent segments information into distinct categories using taxonomies that separate sentiment phrases from non-sentiment phrases, and further divide content into relevant vs. irrelevant categories. This segmentation allows users to access comprehensive information while filtering out unwanted content through structured classification.
Solution Approach 2:
The patent introduces taxonomies as an intermediary layer between raw internet information and users. These taxonomies act as mediators that organize, categorize, and filter information, enabling users to access comprehensive data while automatically excluding irrelevant content through the structured classification system.
2Quantity of substance
If search systems store all collected data, then data completeness is improved, but data processing efficiency deteriorates due to the need to sift through large amounts of data
Solution Approach 1:
The patent segments stored data into organized taxonomies with distinct categories for sentiment and non-sentiment phrases. This segmentation allows the system to maintain complete data while improving processing efficiency through structured organization, enabling faster retrieval and analysis of specific information types without scanning entire datasets.
Solution Approach 2:
The patent performs preliminary organization and classification of data into taxonomies during the data collection phase. By pre-organizing data into structured categories before users need to access it, the system maintains data completeness while significantly improving subsequent processing and retrieval efficiency.
3Quantity of substance
If advertisers target customers based on browsing behavior, then advertising reach is improved, but advertising relevance deteriorates due to inability to distinguish intent
Solution Approach 1:
The patent introduces sentiment analysis taxonomies as an intermediary that bridges the gap between browsing behavior and advertising relevance. By analyzing the sentiment and context of user-generated content, the system can distinguish between users merely browsing and those with genuine purchase intent, enabling more relevant advertising targeting.
Solution Approach 2:
The patent changes the targeting parameters from simple browsing behavior metrics to sentiment-based classifications. By using sentiment phrases and their associations as targeting parameters, the system can identify users with specific intents and attitudes, dramatically improving advertising relevance while maintaining broad reach.
Data Source
AI summary
A system for creation of term taxonomies by mining web based user generated content according. The system includes a network interface enabling access to one or more data sources; a mining unit for collecting textual content from the one or more sources and generating phrases, the generated phrases include sentiment phrases and non-sentiment phrases; an analysis unit for generating at least associations between a non-sentiment phrase and a sentiment phrase based on the generated phrases, wherein an association between a non-sentiment phrase and at least one corresponding sentiment phrase is a taxonomy; and storing the taxonomies in a data warehouse storage connected to the network wherein responsive to a query the system provides a sentiment to a non-sentiment phrase provided in the query.