Emotion Analysis Using Deep Learning Models for Customer Communications
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Solution Overview
Problem
Existing sentiment analysis systems in customer service contact centers are limited in their ability to accurately classify the emotional content of customer communications, often relying on simplistic positive, negative, or neutral classifications that do not capture the nuanced emotional states of customers.
Innovation Solution
The use of an emotion-based indexer deep learning model and an emotion classifier to analyze customer communications, generating emotion factor values for determination, inquisitiveness, valence, and aggression, which are then used to classify the communication into a specific emotional state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis systems are used to classify customer communications, then the system complexity remains low, but the measurement precision of emotional content is insufficient
Solution Approach 1:
The patent segments the emotion analysis task into multiple independent deep learning models, each specialized in detecting a specific emotion factor (determination, inquisitiveness, valence, aggression). This segmentation allows each model to focus on one emotional dimension, improving measurement precision while keeping individual model complexity manageable. The overall system complexity is organized through this modular structure.
Solution Approach 2:
The patent transitions from traditional single-dimension sentiment analysis (positive/negative/neutral) to a multi-dimensional emotion analysis framework. By introducing four distinct emotion factor dimensions, the system achieves higher measurement precision in capturing nuanced emotional states. This dimensional expansion resolves the contradiction by providing more granular emotional classification capability.
2Measurement precision
If deep learning models with multiple emotion factors are used, then the measurement precision of emotional states improves, but the loss of computational resources increases
Solution Approach 1:
The computational workload is segmented across multiple specialized deep learning models, each handling a specific emotion factor. This segmentation allows for more efficient resource utilization compared to a single monolithic model, as each model can be optimized independently and processed in parallel, reducing overall computational resource consumption while maintaining high measurement precision.
Solution Approach 2:
The system changes the parameters of the analysis by focusing on specific emotion factors (determination, inquisitiveness, valence, aggression) rather than attempting to analyze all possible emotional dimensions simultaneously. This parameter selection optimizes the balance between measurement precision and computational resource usage by concentrating resources on the most relevant emotional dimensions for customer service contexts.
3Ease of operation
If simplistic positive-negative-neutral classification is used, then the ease of operation is high, but the loss of information about nuanced emotional states occurs
Solution Approach 1:
The patent adds multiple dimensions to the emotion classification system by introducing four emotion factors (determination, inquisitiveness, valence, aggression) alongside the traditional sentiment dimensions. This multi-dimensional approach preserves nuanced emotional information that would be lost in simple three-category classification, while the systematic structure maintains operational ease through organized output interpretation.
Solution Approach 2:
Different aspects of emotional content are analyzed with different levels of detail appropriate to each dimension. Each emotion factor is measured and classified with specialized attention to its unique characteristics, preserving local nuances in emotional expression. This local quality approach ensures that subtle emotional variations are captured without overwhelming the overall system complexity.
Data Source
AI summary
Techniques are described for generating a set of emotion factor values using one or more machine learning models for customer communications. For example, a computing system includes one or more processors in communication with a memory. The one or more processors are configured to receive communication data of a current communication associated with a customer, apply the communication data to an emotion-based indexer as input wherein the emotion-based indexer includes a set of machine-learning models for a set of emotion factors, generate as output from the emotion-based indexer a set of emotion factor values for the current communication wherein each emotion factor value indicates the measure of a particular emotion factor in the current communication, apply the set of emotion factor values to an emotion classification model, and classify the current communication into an emotion state based on the set of emotion factor values.