Sentiment Analysis for Dynamic Communication Session Adaptation
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Solution Overview
Problem
Users face challenges in identifying desired content on electronic commerce platforms due to overwhelming selections, leading to customer drop-out and loss of sales, as existing search and filtering processes are cumbersome and often fail to yield favorable outcomes.
Innovation Solution
A computer system analyzes user sentiment during communication sessions using a machine learning model to predict the likelihood of terminating a session without performing a desired action, presenting targeted queries to users based on their feedback, and dynamically modifying the session to retain their attention and encourage desired actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search and filtering processes are used to help users identify desired content, then users can access a wide selection of offerings, but the process becomes overwhelming and cumbersome, leading to customer drop-out
Solution Approach 1:
The system continuously monitors user sentiment during communication sessions and uses this feedback to dynamically adjust the interaction. Sentiment analysis of user responses to queries allows the system to detect frustration or disengagement early, enabling real-time adaptation of the search and filtering process to prevent customer drop-out while maintaining ease of operation.
Solution Approach 2:
The communication session is dynamically modified based on user sentiment and machine learning model predictions. The system transitions from static search interfaces to adaptive conversations, adjusting query types, offering recommendations, and modifying session parameters in real-time based on user responses, thereby resolving the contradiction between providing versatile selection and maintaining operational simplicity.
2Measurement precision
If the system presents more search options and filtering criteria to help users find desired content, then content identification capability improves, but user overload increases and desired actions decrease
Solution Approach 1:
Instead of presenting all possible search options and filtering criteria simultaneously, the system uses machine learning to identify and present only the most relevant partial set of options based on user sentiment and behavior. This selective approach maintains content identification accuracy while preventing user overload, thereby preserving productivity and desired action completion rates.
Solution Approach 2:
The system dynamically changes parameters of the search and filtering process based on user sentiment analysis. When sentiment indicates user stress or disengagement, the system adjusts parameters such as reducing the number of displayed filters, simplifying query complexity, or shifting from broad search to targeted recommendations, thus maintaining identification precision without compromising action completion.
3Reliability
If the system uses machine learning models to predict user behavior and personalize interactions, then user retention improves, but system complexity increases
Solution Approach 1:
The system introduces sentiment analysis as an intermediary layer between the machine learning model and the user interaction. Rather than requiring complex models to directly interpret all user behaviors, the sentiment analysis component serves as a mediator that processes user responses and provides simplified signals to the ML model, improving user retention while managing system complexity through modular architecture.
Data Source
AI summary
A computer system analyzes user sentiment to dynamically modify a communication session. One or more user interactions are captured during a communication session, wherein a machine learning model is updated based on the captured user interactions. A likelihood score of a user terminating the communication session before performing one or more desired actions is calculated. In response to determining that the likelihood score is above a threshold value, one or more queries are presented, during the communication session, to the user, wherein the one or more queries are selected using the machine learning model. Received user feedback is analyzed to determine a user sentiment. The communication session is dynamically modified based on to the user feedback and the user sentiment. Embodiments of the present invention further include a method and program product for analyzing user sentiment to dynamically modify a communication session in substantially the same manner described above.