Real-Time Mood Metrics Tracking in Agent-Customer Text Conversations
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Solution Overview
Problem
Conventional mood mining techniques are inadequate for predicting and tracking mood changes in goal-directed textual conversations between agents and customers, failing to provide insights necessary for achieving target outcomes in online chat interactions.
Innovation Solution
A computer-implemented method and apparatus that determine and track mood metrics across multiple chat stages of real-time textual conversations, using algorithms like lexicon-based sentiment analysis and supervised text classification to predict changes and inform actions that drive conversations towards desired outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mood mining techniques are used to analyze textual conversations, then overall sentiment prediction is achieved, but real-time mood change tracking across chat stages is inadequate
Solution Approach 1:
The patent segments the textual conversation into multiple chat stages (e.g., greeting, problem identification, solution provision, closing) and calculates mood metrics separately for each stage. This segmentation enables tracking of mood changes across different phases of the conversation, providing granular insights that conventional overall sentiment analysis cannot deliver.
2Loss of information
If detailed mood analysis is performed across multiple chat stages, then customer intent and satisfaction insights are improved, but computational complexity and processing time increase
Solution Approach 1:
The system implements feedback mechanisms where mood metrics from previous chat stages inform the analysis of subsequent stages. The mood change between stages is calculated and used to trigger specific actions or alerts, creating a feedback loop that improves customer intent understanding while managing computational complexity through selective analysis.
3Productivity
If real-time mood tracking is implemented in goal-directed conversations, then conversation effectiveness is enhanced, but processing speed and response time may be reduced
Solution Approach 1:
The patent pre-processes and stores mood metrics for each chat stage as the conversation progresses, rather than performing comprehensive analysis after the conversation ends. This preliminary action enables real-time tracking of mood changes and allows for immediate intervention or action taking based on detected mood patterns, maintaining both effectiveness and speed.
Data Source
AI summary
In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.