Keyword Clustering for Online Data Classification
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Solution Overview
Problem
Conventional methods for classifying online user-generated data in forums often fail to capture the complexity of user queries, leading to incomplete information retrieval due to their reliance on maximum similarity-based approaches that neglect users' multiple interests and the noisy, unstructured nature of online data.
Innovation Solution
A method involving keyword extraction, clique percolation modeling, and extended community formation to identify overlapping clusters based on keyword correlations, allowing for the inclusion of posts and users with varying degrees of similarity, thereby creating more comprehensive and accurate community structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If maximum similarity-based classification is used, then classification speed is improved, but information completeness deteriorates because multiple user interests are not captured
Solution Approach 1:
The patent segments the classification task into multiple dimensions by extracting multiple keywords and concepts from each post, then classifying posts based on their relationships with multiple keyword clusters rather than a single similarity metric. This allows simultaneous consideration of different user interests while maintaining efficient processing through automated keyword extraction and clustering.
Solution Approach 2:
The patent transitions from traditional single-dimension similarity classification to a multi-dimensional classification approach by creating keyword clusters that capture multiple concepts and interests. Each post is evaluated against multiple keyword clusters, adding dimensional complexity that enables comprehensive information retrieval while maintaining computational efficiency through automated processing.
2Device complexity
If conventional similarity-based approaches are used, then system complexity is reduced, but adaptability to multiple user interests deteriorates
Solution Approach 1:
The patent performs preliminary keyword extraction and clustering actions on existing forum posts to build a comprehensive keyword cluster database before new posts arrive. This pre-processing creates a structured multi-dimensional classification framework that can adapt to various user interests without increasing operational complexity, as the system simply queries pre-built clusters rather than analyzing everything in real-time.
Solution Approach 2:
The patent introduces keyword clusters as intermediary structures between user posts and classification results. These keyword clusters serve as mediators that capture multiple interests and concepts, allowing the system to handle diverse user queries through a unified framework without requiring complex real-time analysis for each individual post.
3Measurement precision
If overlapping communities are formed to capture multiple interests, then information relevance is improved, but data processing complexity increases
Solution Approach 1:
The patent implements self-service processing where the system automatically extracts keywords, forms clusters, and classifies posts without manual intervention. The automated keyword extraction and clustering algorithms process data independently, reducing the need for human oversight in complex multi-interest classification while maintaining high information relevance through comprehensive automated analysis.
Data Source
AI summary
A method of automatically analyzing online posts, such that they may then be responded appropriately. The method may comprise: extracting a list of keywords from each of the plurality of posts; generating one or more keyword clusters based on the keywords extracted from each of the plurality of posts and classifying new posts in accordance with the one or more keyword clusters.