Text Analytics System for Context-Dependent Inappropriate Content Detection
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Solution Overview
Problem
Current systems for detecting inappropriate content in text-based user interactions are inefficient due to the need for manual updates of dictionaries or blacklists, which are time-consuming and prone to errors, and fail to identify variants or context-dependent inappropriate content.
Innovation Solution
An automated system that analyzes unstructured text inputs using text analytics techniques, including natural language processing, to identify inappropriate content by checking against pre-categorized content and generating a content score based on responses from other users, allowing for context-dependent identification and action without manual updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a manual dictionary or blacklist is used to detect inappropriate content, then the system can identify known obscene words, but the system requires time-consuming manual updates and cannot identify variants or context-dependent inappropriate content
Solution Approach 1:
The system enables self-service by automatically learning inappropriate content patterns from user interactions and context analysis. Instead of requiring manual dictionary updates, the system autonomously identifies new inappropriate words, phrases, and variants by analyzing user responses and contextual usage patterns, thereby eliminating time-consuming manual updates while maintaining high detection accuracy
Solution Approach 2:
The system implements feedback mechanisms by analyzing user responses to detected content and continuously improving its detection capabilities. User reactions and contextual feedback are processed to refine the inappropriate content detection model, enabling the system to adapt to new variants and context-dependent inappropriate content without manual intervention
2Productivity
If a static dictionary is used to detect inappropriate content, then the system can quickly compare words against known obscene terms, but the system fails to identify variants or context-dependent inappropriate content
Solution Approach 1:
The system transitions from a static dictionary to a dynamic detection model that adapts to different contexts and content variants. The machine learning model continuously learns from user interactions and contextual patterns, enabling it to maintain fast detection speeds while simultaneously improving adaptability to new inappropriate content forms and context-dependent meanings
Solution Approach 2:
The system changes the parameters of content evaluation by moving beyond simple keyword matching to multi-dimensional analysis including contextual semantics, user response patterns, and behavioral indicators. This allows the system to maintain efficient processing while detecting a broader range of inappropriate content including variants and context-dependent expressions
3Reliability
If conventional keyword matching is used, then the system can identify explicitly obscene words, but the system cannot detect inappropriate content that is rude or offensive in specific contexts
Solution Approach 1:
The system introduces contextual analysis as an intermediary layer between keyword detection and inappropriate content identification. By analyzing user responses, conversation context, and behavioral patterns, the system mediates between simple keyword matching and nuanced inappropriate content detection, maintaining reliability while gaining context awareness
Data Source
AI summary
Embodiments provide a method for detecting inappropriate content in user interactions, including: receiving an unstructured text-based input corresponding to a user interaction of a user; analyzing, using a text analytics technique, the text-based input to identify content within the input; determining whether at least a portion of the content within the input comprises inappropriate content by determining if the at least a portion of the content is categorized as inappropriate content; if the content is categorized as inappropriate content, identifying the content as inappropriate content; and if the content is not categorized as inappropriate content, receiving text-based input from other users, analyzing the text-based input from other users to determine a sentiment of the text-based input from other users, generating a content score for the content of the user, and identifying the content as inappropriate content if the content score meets or exceeds a predetermined threshold; and performing an action.