Tone Latent Dirichlet Allocation for Emotional Tone Analysis
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Solution Overview
Problem
Existing online customer service systems struggle to effectively analyze and manage emotional tones in customer interactions, particularly in asynchronous interactions on social media platforms like Twitter, where emotional responses from customers need to be properly addressed and pacified.
Innovation Solution
The implementation of a Tone Latent Dirichlet Allocation (T-LDA) model that analyzes tone intensity using emotional tone factors, integrates adjusted labeled data, and provides representative words for each emotional tone factor, enabling better understanding and management of customer emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text analysis methods are used in online customer service, then implementation is simple, but emotional tone analysis capability is insufficient
Solution Approach 1:
The patent segments emotional tone analysis into multiple independent components: emotion detection module, tone intensity analysis module, and sentiment classification module. Each module processes specific aspects of emotional tone separately, enabling comprehensive analysis while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The patent introduces an intermediary emotional tone model that bridges raw customer text and agent response strategies. This model acts as a mediator that transforms unstructured text into structured emotional tone data, enabling downstream systems to make informed decisions without directly processing complex raw text.
2Ease of operation
If asynchronous online interaction is used, then customer convenience is improved, but emotional pacification becomes more difficult
Solution Approach 1:
The patent implements preliminary emotional tone analysis on incoming customer messages before agents respond. By detecting and analyzing emotional tones in advance, the system prepares appropriate response strategies proactively, enabling agents to address customer emotions effectively even in asynchronous interactions where immediate verbal cues are unavailable.
Solution Approach 2:
The patent incorporates feedback mechanisms where emotional tone analysis results from previous interactions inform subsequent response strategies. The system continuously learns from emotional tone patterns in customer responses, adjusting and refining pacification strategies over time to improve effectiveness in asynchronous communication.
Data Source
AI summary
One embodiment provides a method that includes receiving adjusted labeled data based on emotional tone factors. Words are analyzed using a tone latent Dirichlet allocation (T-LDA) model that models tone intensity using the emotional tone factors and integrating the adjusted labeled data. Representative words are provided for each emotional tone factor based on using the T-LDA model. The representative words are obtained using the T-LDA model based on determining posterior probabilities and adjusting the posterior probabilities based on an auxiliary topic.