Rule-Based Emotio-Cognition Detection Engine
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Solution Overview
Problem
Current sentiment analysis technologies face challenges such as reliance on manually classified training datasets, which are slow, expensive, and prone to errors; inability to accommodate creativity in language; and difficulty in measuring the intensity of emotions due to limited effective methods and unrepresentative datasets.
Innovation Solution
A method using a rule-based engine implemented by processing circuitry to detect psychological affects in natural language content, involving preprocessing, searching for matches with linguistic rules, scoring human dimensions, aggregating scores for intensity indication, and displaying the content with matched rules and intensity indications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual classification methods are used to create training datasets, then the data can be obtained from human raters, but the process becomes slow, expensive, and error-prone
Solution Approach 1:
The system uses automated sentiment analysis algorithms to classify text data without requiring continuous human intervention. The model trains on initially manually labeled data, then automatically classifies subsequent data points, reducing the need for ongoing manual annotation while maintaining classification quality.
Solution Approach 2:
The patent replaces the mechanical process of manual human classification with an automated computational system. Machine learning models and natural language processing algorithms substitute for human raters, eliminating the limitations of manual classification while preserving the ability to handle complex linguistic nuances.
2Reliability
If machine learning models are trained on static datasets, then the models can achieve good performance on known patterns, but they fail to accommodate evolving natural language and creative expressions
Solution Approach 1:
The system implements dynamic, continuously updating training datasets that adapt to evolving language patterns. Instead of relying on static historical data, the model incorporates new data streams and updates its parameters over time, allowing it to accommodate creative expressions, slang, and linguistic changes while maintaining performance on established patterns.
Solution Approach 2:
The patent establishes a continuous feedback loop where the model is constantly trained on new data, ensuring uninterrupted adaptation to language evolution. The training process is ongoing rather than periodic, allowing the system to maintain both reliability on known patterns and adaptability to new expressions simultaneously.
3Ease of manufacture
If sentiment analysis systems use lexicon-centric or token-based solutions, then the implementation is straightforward, but the systems lack staying power due to the dynamic nature of language
Solution Approach 1:
The system moves beyond fixed lexicon-based parameters to dynamic, context-aware parameter representation. Instead of relying on static word lists and token matching, the model uses distributed representations and contextual embeddings that adapt to changing language usage, maintaining both implementation feasibility and long-term reliability through parameter flexibility.
Data Source
AI summary
A system and method for detecting a psychological affect in a natural language content with a rule-based engine includes receiving the natural language content as a textual input; searching for matches between linguistic rules for a given emotio-cognition and components of the natural language content, wherein instances of the linguistic rules have human dimensions; activating the matched linguistic rules, and evaluating the human dimensions of the matched rules; scoring each human dimension to obtain a profile of dimension scores for the given emotio-cognition; aggregating the dimensions in the obtained profile of dimension scores to obtain an intensity indication for the given emotio-cognition; and displaying the natural language content in a manner that relates the matched linguistic rules in conjunction with the given emotio-cognition and respective intensity indication of the given emotio-cognition.