Automated Topic Extraction for Consultation Category Creation
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Solution Overview
Problem
Online consultation systems face challenges in identifying and addressing under-served or un-served categories of questions, leading to a lack of qualified experts to provide timely responses, which affects the quality of service.
Innovation Solution
The system analyzes unanswered questions to automatically identify relevant topics and keywords, creating new categories or recruiting experts to service these topics, and uses these keywords for promoting categories through online advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of under-served categories is used, then expert recruitment can be targeted, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system automatically analyzes question data to identify under-served categories without human intervention. The automated analysis engine processes question texts, extracts topics, and identifies categories that lack sufficient expert coverage, enabling the system to self-diagnose and self-improve its category structure
Solution Approach 2:
Manual text analysis and category identification by human operators is replaced with automated computational methods. The system uses text mining, topic extraction algorithms, and data analysis to automatically identify under-served categories, substituting mechanical human labor with automated information processing
2Productivity
If automated topic extraction is implemented, then category identification efficiency improves, but system complexity increases
Solution Approach 1:
The automated analysis system is divided into distinct functional modules: question data collection, text preprocessing, topic extraction engine, category matching module, and reporting system. Each module performs a specific function, making the complex system manageable and maintainable while achieving high productivity
Solution Approach 2:
The automated topic extraction system serves multiple functions: identifying under-served categories, extracting relevant topics from questions, generating reports for expert recruitment, and providing insights for category optimization. This multi-functionality justifies the system complexity by delivering comprehensive value
3Reliability
If existing category structure is maintained, then system stability is preserved, but under-served categories cannot be addressed
Solution Approach 1:
The system continuously monitors question data to identify under-served categories and provides feedback for category structure optimization. By analyzing patterns in unanswered or under-answered questions, the system generates insights that feed back into category management, enabling continuous improvement while maintaining overall system stability
Solution Approach 2:
The category structure is treated as dynamic rather than static. The system enables adaptive category creation and modification based on emerging topics and changing user needs, allowing the category framework to evolve organically while maintaining the stability of well-established categories
Data Source
AI summary
Embodiments of the present invention further provide systems and methods for automatically identifying and extracting topics from questions posted by users to a consultation system, for creating a new consultation category or creating new keywords for of promotion of a new category serving an underserved or unserved topic.