Hypocrisy Detection in Text via Syntactic and Discourse Trees
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Solution Overview
Problem
Current computer-implemented linguistics solutions are inadequate in fully analyzing textual inputs, particularly in identifying hypocrisy, which is essential for understanding user sentiments and interactions.
Innovation Solution
The method creates syntactic and communicative discourse trees to identify entities and determine sentiment scores, using ontology, search engines, and machine-learning models to detect hypocrisy by analyzing the relationships between words and emotions in text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer-based analysis of language discourse is implemented, then automated agents can answer questions from user devices, but the solution is unable to completely analyze textual inputs and identify hypocrisy
Solution Approach 1:
The patent segments textual input into multiple discourse trees, each representing different aspects of the text. This allows the system to analyze various components separately and comprehensively, enabling complete textual analysis including hypocrisy detection while maintaining automated processing efficiency.
Solution Approach 2:
The patent introduces discourse trees as intermediary structures between raw text and analysis results. These trees serve as mediators that organize textual information hierarchically, enabling the automated agent to systematically process and completely analyze textual inputs for hypocrisy while preserving productivity.
2Loss of information
If sentiment detection is performed to identify hypocrisy, then user sentiments can be understood, but the complexity of analysis increases
Solution Approach 1:
The patent segments sentiment analysis into multiple discourse trees, each focusing on specific aspects of user input. This segmentation allows comprehensive sentiment understanding while distributing analytical complexity across modular structures, making the system more manageable and efficient.
Solution Approach 2:
The patent adds a hierarchical dimension to sentiment analysis by organizing text into discourse trees with multiple levels. This dimensional transformation enables comprehensive sentiment detection while structuring complexity in a manageable hierarchical framework rather than flat processing.
Data Source
AI summary
Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.