Real-Time Sentiment Scoring in Interactive Voice Response Systems
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Solution Overview
Problem
Interactive voice response systems lack the ability to effectively determine and score the sentiment trajectory of human interactions in real time, which limits their capacity to provide appropriate responses based on fluctuating human emotions during conversations.
Innovation Solution
An interactive voice response system that computes a sentiment score in real time by analyzing interaction pairs, using a multi-class classifier to determine utterance and conversation sentiment categories, and routing responses accordingly based on these categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-time sentiment analysis is implemented, then response appropriateness improves, but system complexity increases
Solution Approach 1:
The sentiment analysis system is segmented into distinct functional modules: a sentiment analysis module that processes individual utterances, a conversation state manager that maintains context across turns, and a response generator that produces appropriate responses. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while enabling real-time sentiment-based adaptation.
Solution Approach 2:
The system performs preliminary sentiment analysis on each utterance before generating responses. By analyzing sentiment trajectories in advance and categorizing conversations into sentiment states (e.g., frustrated, satisfied, confused), the system prepares appropriate response strategies beforehand, enabling real-time adaptation without adding significant complexity to the response generation process.
2Measurement precision
If real-time sentiment scoring is computed, then conversation understanding improves, but processing time increases
Solution Approach 1:
The system computes sentiment scores for individual utterances and maintains a running sentiment trajectory rather than analyzing the entire conversation history at once. This partial action approach allows the system to capture sentiment changes in real-time without processing the complete conversation context, thereby improving measurement precision while minimizing processing time delays.
Solution Approach 2:
The sentiment analysis operates continuously as conversations unfold, with the sentiment analysis module processing each utterance immediately upon reception. The conversation state manager continuously updates the sentiment trajectory based on incoming utterances, enabling real-time understanding without batch processing delays. This continuous action ensures conversation understanding is always current while maintaining fast processing speeds.
3Reliability
If sentiment trajectory tracking is implemented, then response accuracy improves, but computational resources increase
Solution Approach 1:
The system applies different levels of computational analysis to different parts of the conversation. Individual utterances receive detailed sentiment analysis with multiple features (word choice, syntax, semantic meaning), while the overall conversation trajectory is summarized using fewer computational resources. This local quality approach ensures high response accuracy for critical sentiment detection while reducing overall computational resource consumption.
Solution Approach 2:
The system dynamically adjusts the level of computational analysis based on conversation context. When sentiment trajectory changes are detected or critical conversation states are identified, the system increases computational resource allocation to analyze those specific utterances more deeply. For routine conversations, the system uses lighter computational processing, thereby maintaining response accuracy while optimizing computational resource usage.
Data Source
AI summary
An interactive voice response system for interpreting sentiment of a conversation in real time is provided. The system may include a receiver. The receiver may be operable to receive a plurality of sentiment signals and a first portion of a conversation. The first portion of the conversation may include a plurality of interaction pairs. Each interaction pair may include an utterance and a system response. The system may include an utterance sentiment category determination module that may be operable to determine a sentiment category for each utterance. The system may include a sequential module. The sequential module may determine a conversation sentiment category based on the utterance sentiment categories and the order in which the utterance sentiment categories are received. The system may include an output module. The output module may output the conversation sentiment category. The conversation sentiment category may be used to determine a future system response.