Voice Sentiment Analysis via Emotion Extraction and Aggregation
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Solution Overview
Problem
Current systems lack the ability to effectively analyze and aggregate emotional content from customer interactions, such as voice communications, alongside relevant attribute information, to assess customer sentiment and product/service quality.
Innovation Solution
A system comprising a voice analysis device, an attribute storage device, and an aggregation server that detects voice emotion and associates it with attribute information, generating reports to assess customer interactions and sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If companies record and store all customer interaction data, then they have more information available for analysis, but the complexity and cost of data management increases significantly
Solution Approach 1:
The system extracts only the essential emotional content and sentiment information from recorded customer interactions using voice analysis technology. Instead of managing and analyzing all raw interaction data, the system isolates and extracts only the emotional attributes (positive/negative sentiment, emotional intensity) which are then stored and analyzed separately. This extraction approach reduces data management complexity while preserving the critical sentiment information needed for quality assessment.
2Measurement precision
If manual analysis of recorded interactions is performed, then detailed sentiment assessment is possible, but the time and resources required increase significantly
Solution Approach 1:
The system replaces the mechanical process of manual human analysis with automated voice analysis technology. The voice analysis device automatically processes recorded interactions, detecting emotional content and sentiment without human intervention. This substitution maintains measurement precision by using sophisticated algorithms to identify emotional patterns, while dramatically reducing the time required for analysis from hours of manual review to rapid automated processing.
Solution Approach 2:
The system enables self-service sentiment analysis where the voice analysis device autonomously processes and evaluates customer interactions without requiring human analysts. The automated system independently detects emotional content, generates sentiment assessments, and provides quality metrics, freeing human resources from routine analysis tasks while maintaining consistent and scalable analysis capabilities.
3Productivity
If voice analysis technology is implemented, then emotional content can be detected automatically, but the complexity of integrating multiple devices and systems increases
Solution Approach 1:
The system merges the voice analysis device, attribute storage device, and reporting device into an integrated architecture where components communicate through standardized interfaces. The voice analysis device processes audio inputs and outputs sentiment data that is automatically stored in the attribute storage device and processed by the reporting device. This merging approach consolidates multiple functions into a unified system, reducing integration complexity compared to separate standalone systems while maintaining high productivity through automated emotional content detection and reporting.
Data Source
AI summary
A system is configured to receive voice emotion information, related to an audio recording, indicating that a vocal utterance of a speaker is spoken with negative or positive emotion. The system is configured to associate the voice emotion information with attribute information related to the audio recording, and aggregate the associated voice emotion and attribute information with other associated voice emotion and attribute information to form aggregated information. The system is configured to generate a report based on the aggregated information and one or more report parameters, and provide the report.