Multi-Channel Sentiment Analysis Engine for Real-Time Customer Support
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional sentiment analysis solutions are ineffective in gauging customer sentiment during communication sessions with support centers, such as phone calls or AI-based chats, as they are typically trained on whole documents and cannot handle real-time data from multiple communication channels.
Innovation Solution
A sentiment analysis engine that includes a data acquisition engine to collect data from various channels, a preprocessing engine for formatting, and a customization engine for channel-specific interpretations, along with a batch scoring engine to quantify sentiments in real-time, displayed via a graphical user interface for agents or supervisors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional sentiment analysis solutions are trained on whole documents, then they can analyze sentiment in news articles, reviews, or forum posts, but they cannot gauge customer sentiment in real-time during communication sessions across multiple channels
Solution Approach 1:
The system segments the sentiment analysis process into separate modules: a data acquisition engine for collecting data from multiple channels, a preprocessing engine for formatting and filtering, a customization engine for channel-specific processing, and a batch scoring engine for sentiment quantification. This segmentation allows each component to be optimized for its specific function while working together to achieve real-time multi-channel sentiment analysis.
Solution Approach 2:
The sentiment analysis engine is designed as a universal system that can process data from multiple communication channels (phone calls, agent chats, AI bot chats, emails, social media) through a single integrated platform. The data acquisition engine collects data from all channels, and the subsequent processing engines handle the data in a unified manner, enabling the system to serve multiple functions across different communication mediums.
2Productivity
If the system processes data from multiple communication channels in real-time, then it can provide timely sentiment feedback to agents, but it increases system complexity
Solution Approach 1:
The system divides the complex real-time processing task into sequential stages: data acquisition, preprocessing, customization, and batch scoring. Each stage handles a specific aspect of the data processing pipeline, making the overall complex system more manageable and easier to implement while maintaining real-time processing capability through efficient stage-to-stage data flow.
Solution Approach 2:
The preprocessing engine performs formatting and filtering operations on incoming data before it reaches the customization and scoring stages. This preliminary action prepares the data in advance, reducing the processing burden on subsequent stages and enabling faster real-time sentiment analysis while keeping each individual stage relatively simple.
3Measurement precision
If the system applies channel-specific customizations to interpret data differently for each communication channel, then it improves sentiment analysis accuracy, but it increases processing time
Solution Approach 1:
The customization engine applies channel-specific customizations as a preliminary step before the batch scoring engine performs sentiment quantification. By pre-processing and interpreting channel-specific nuances (such as tone detection for voice calls or emoji interpretation for chat) in advance, the system prepares the data for faster and more accurate sentiment scoring while maintaining the time efficiency needed for real-time analysis.
Data Source
AI summary
Techniques described herein are directed to a sentiment analysis engine for classifying and quantifying customer sentiments between a customer and an agent. The sentiment analysis engine includes a data acquisition engine configured to receive data from a plurality of different communication channels. A preprocessing engine of the sentiment analysis engine is configured to apply rule(s) that are configured to perform formatting and/or filtering operations on the received data and/or isolate customer-provided messages from agent-provided messages. A customization engine of the sentiment analysis engine is configured to perform channel-specific customizations on the received data, such as, but not limited to interpreting the data in a channel-specific manner A batch scoring engine of the sentiment analysis engine is configured to quantify customer sentiments in real-time (e.g., during a communication session between the customer and agent), and such sentiments may be displayed via a GUI utilized by an authorized individual.