Neural Network Emotion Detection Using Lexicon Characteristic Matrices
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Solution Overview
Problem
Current methods for detecting emotions and reactions in electronic communication are unreliable and inconsistent, particularly for customer service and marketing, due to the complexity of existing solutions which can hinder the effectiveness of artificial intelligence systems.
Innovation Solution
A method and system using a neural network model trained with annotated text strings to generate a characteristic matrix from user-generated content, which is then analyzed to determine emotional cues, employing a lexicon and emotion representative dictionary to provide accurate and reliable emotion prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex existing solutions are used for emotion detection, then detection capability may be enhanced, but reliability and consistency deteriorate
Solution Approach 1:
The patent extracts and focuses on specific linguistic features (emotion-related words, phrases, and patterns) from the complex text data, using a targeted lexicon approach rather than attempting to analyze all aspects of electronic communication. This extraction of relevant features improves reliability by concentrating computational resources on the most indicative signals.
Solution Approach 2:
The system changes the parameters of analysis by using predefined emotion lexicons and characteristic tuples that capture specific emotional dimensions. Rather than attempting comprehensive semantic analysis, the system transforms the problem into detecting specific emotional parameters through targeted feature extraction and pattern matching.
2Measurement precision
If complex existing solutions are used for emotion detection, then detection capability may be enhanced, but device complexity increases
Solution Approach 1:
The patent segments the emotion detection task into distinct components: lexicon-based feature extraction, characteristic tuple generation, and neural network classification. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining detection capability.
Solution Approach 2:
The system introduces characteristic tuples as an intermediary representation between raw text input and neural network processing. These tuples serve as a simplified bridge that captures essential emotional features without requiring the neural network to directly process complex unstructured text, thereby reducing overall system complexity.
3Adaptability or versatility
If manual emotion detection by individuals is used, then flexibility is maintained, but consistency deteriorates
Solution Approach 1:
The system enables automated self-service emotion detection that operates consistently without human intervention. The neural network model, trained on annotated data, automatically processes electronic communication and generates emotion predictions, eliminating variability introduced by different human analysts while maintaining adaptability through the flexible lexicon and characteristic tuple framework.
Data Source
AI summary
A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.