Emotion-Based Complaint Prioritization Using Deep Learning
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Solution Overview
Problem
Customer service contact centers face challenges in prioritizing customer inquiries effectively, as existing systems lack a systematic way to identify and order inquiries based on severity, often handling them on a first-in, first-out basis without regard to emotional urgency.
Innovation Solution
A computing system employs an emotion-based indexer deep learning model and emotion classifier to determine emotional priority scores for customer communications, using historical data to classify and prioritize service inquiries, allowing for a more urgent response to pressing issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customer service inquiries are handled on a first-in, first-out basis, then the processing order is simple and systematic, but the emotional urgency and severity of complaints are not properly prioritized
Solution Approach 1:
The patent transforms the单一的 FIFO processing parameter into a multi-dimensional prioritization system by introducing emotional intensity scores, sentiment analysis results, and severity classifications as additional sorting parameters. This allows the system to dynamically adjust prioritization based on the emotional state and urgency of each customer inquiry rather than relying solely on arrival time.
Solution Approach 2:
The patent introduces an intermediary prioritization system that sits between the incoming customer inquiries and the service agents. This intermediary layer analyzes emotional content, determines severity levels, and re-ranks inquiries before presenting them to agents, thereby mediating between the simple FIFO input and the need for emotionally-aware prioritization output.
2Reliability
If an emotion-based prioritization system is implemented, then the emotional urgency of complaints is accurately identified, but the system complexity increases
Solution Approach 1:
The patent replaces manual or rule-based prioritization mechanisms with automated natural language processing and sentiment analysis systems. These computational systems automatically extract emotional content, determine sentiment polarity, and calculate urgency scores from customer inquiry text, eliminating the need for manual emotional assessment and reducing operational complexity despite increasing computational requirements.
Solution Approach 2:
The prioritization system performs self-service by automatically analyzing its own input data through embedded sentiment analysis algorithms. The system autonomously determines emotional intensity and severity without requiring external manual intervention, thereby managing its own prioritization function while maintaining accuracy.
3Loss of information
If sentiment analysis algorithms are applied to customer communications, then emotional information is extracted, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial sentiment analysis by focusing on key emotional indicators and sentiment-carrying phrases rather than analyzing every word in customer communications. The system identifies and weights specific sentiment-bearing terms and expressions, performing sufficient emotional analysis to determine urgency without the computational overhead of complete text analysis.
Solution Approach 2:
The system performs preliminary text preprocessing and keyword identification before applying full sentiment analysis algorithms. By pre-identifying sentiment-critical portions of customer inquiries and filtering out non-essential text, the system prepares data in advance to reduce the computational burden and processing time of the main emotional analysis phase.
Data Source
AI summary
Techniques are described for performing complaint prioritization using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to receive communication data indicative of a service inquiry from a user device, generate a set of emotion factor values that indicate a measure of particular emotions in the service inquiry, determine, using a machine learning model and based on the set of emotion factor values, an emotional priority score for the service inquiry, and determine a response priority order for the service inquiry based on at least the emotional priority score.