Conversation Empathy Scoring Using Sentiment and Semantic Transitions
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Solution Overview
Problem
Conventional techniques lack concrete and objective methods for assessing speaker effectiveness in conversations, particularly in business interactions, failing to measure or improve the effectiveness of sales or customer service dialogues.
Innovation Solution
An apparatus and method using AI/ML techniques to determine speaker effectiveness by calculating sentiment transition (ST) and semantic classification (SC) scores, which are used to compute an empathy score based on sentiment and relevance of utterances in conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used for conversation assessment, then the process is simple, but the measurement precision and objectivity of speaker effectiveness is insufficient
Solution Approach 1:
The assessment system segments speaker effectiveness into multiple independent dimensions: sentiment transition scores, semantic classification scores, relevance scores, and empathy scores. Each dimension is calculated separately using specific algorithms, allowing precise measurement of different aspects of conversation quality without requiring a single complex assessment mechanism
Solution Approach 2:
The system introduces AI/ML models as intermediary components that process raw conversation data and transform it into structured effectiveness metrics. These intermediaries (sentiment analysis models, semantic classification models) bridge the gap between simple conversation recording and precise effectiveness measurement, handling the complexity internally while providing clear output metrics
2Measurement precision
If AI/ML techniques are implemented for detailed conversation analysis, then measurement precision improves, but computational resources and processing time increase
Solution Approach 1:
The system calculates only the specific metrics necessary for speaker effectiveness assessment (sentiment transition, semantic classification, relevance) rather than performing comprehensive analysis of all conversation aspects. This partial action approach focuses computational resources on the most impactful dimensions of conversation quality, reducing overall energy consumption while maintaining measurement precision for key effectiveness indicators
Solution Approach 2:
The system transforms raw conversation data into standardized numerical parameters (scores ranging from 0-1 or -1 to 1) that can be efficiently processed and compared. By changing the parameter representation of conversation elements into normalized scores, the system enables efficient computational processing while maintaining precise measurement capability across different conversation contexts
Data Source
AI summary
In a method and an apparatus for determining speaker effectiveness in conversations, the method includes determining a sentiment transition (ST) score in a consecutive speaker turn pair in a conversation between a first speaker and a second speaker. The ST score measures whether the sentiment transition from the first speaker to the second speaker is negative, neutral, or positive. The method further includes determining a semantic classification (SC) score in the speaker turn pair. The SC score measures the relevance of utterances of the second speaker to the utterance of the first speaker. The method further includes determining an empathy score for the second speaker in the speaker turn pair based on the ST score and the SC score.