Emotion Context Analysis for Detecting Advanced Emotions
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Solution Overview
Problem
Current emotion detection technologies, such as tone analyzers, struggle to recognize complex emotions like irony, sarcasm, and Schadenfreude, as they rely on surface-level sentiment analysis and fail to capture the chronology and context of emotions expressed by authors across multiple statements.
Innovation Solution
A method that creates and utilizes emotion context tuples, combining emotion detection with a novel context analysis that tracks time references, sentiment, and topics to classify new emotions, employing both rule-based and machine learning approaches, including neural networks, to identify advanced emotions not detectable by prior art.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface-level sentiment analysis is used for emotion detection, then the system is simple and fast, but it fails to recognize complex emotions like irony, sarcasm, and Schadenfreude
Solution Approach 1:
The patent segments emotion detection into multiple analytical layers: surface-level sentiment analysis, contextual analysis, and chronological analysis. Each layer processes specific aspects of the content independently, then their results are integrated to detect complex emotions. This segmentation allows the system to maintain simplicity in individual components while achieving high precision through their combination.
Solution Approach 2:
The patent adds chronological dimension to emotion detection by analyzing the sequence and timing of emotional expressions across multiple statements. Instead of treating each statement in isolation, the system incorporates time-based context to detect emotions like irony and Schadenfreude that emerge from temporal patterns, thereby increasing detection accuracy without proportionally increasing system complexity.
2Measurement precision
If context analysis tracking chronology and multiple statements is implemented, then complex emotions can be detected, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary segmentation of content into discrete statements with assigned timestamps and surface-level sentiment labels before conducting contextual analysis. This preliminary processing organizes the data structure in advance, enabling more efficient chronological analysis later. By preparing the data framework beforehand, the system reduces the computational burden during the actual complex emotion detection phase.
3Loss of information
If traditional tone analyzers are used, then the system is computationally efficient, but it cannot capture the chronology and context of emotions across multiple statements
Solution Approach 1:
The patent creates a multi-functional analysis system that performs both surface-level sentiment analysis and deep contextual-chronological analysis within a unified framework. The same system architecture handles simple emotions through rapid sentiment analysis while automatically detecting complex emotions through contextual patterns, eliminating the need for separate specialized systems and maintaining high automation across different emotion types.
Data Source
AI summary
A method selectively activates a resource to accommodate an advanced emotion. A supervisor computer receives a first piece of content, and then applies an emotion classifier to the first piece of content in order to create a first concept/emotion/sentiment/time tuple. The supervisor computer creates a second concept/emotion/sentiment/time tuple for a second piece of content, and compares the first and second tuples. If the concept in the first piece of content matches the concept in the second piece of content but that at least one of the emotion, sentiment, and time of the first piece of content does not match the emotion, sentiment, and time of the second piece of content, the supervisor computer determines that the emotion of the second piece of content is an advanced emotion that is not expressed by the first or second pieces of content, and activates a resource that accommodates the advanced emotion.