Sentiment Analysis Pipeline for Multi-Channel Accuracy
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Solution Overview
Problem
Existing systems struggle to accurately determine user sentiment in text-based communications due to the lack of integration of voice and textual cues, hindering effective interaction improvement with customers.
Innovation Solution
A sentiment analysis system that preprocesses unstructured textual data by identifying user and representative portions, removes extraneous data, and utilizes machine-learned models like large language models or convolutional neural networks to generate graphical representations of sentiment trends for improved interaction strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated sentiment determination is implemented in text-based communications, then efficiency is improved, but accuracy deteriorates due to inability to capture voice and textual cues
Solution Approach 1:
The patent combines multiple data sources including text communications, voice communications, and metadata into a unified analysis system. The sentiment determination system integrates textual data from chat transcripts with voice data from call recordings, along with metadata from both channels, to create a comprehensive sentiment assessment that overcomes the limitations of analyzing text alone.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw text and voice data into structured sentiment indicators. This intermediary system processes unstructured communications through multiple analysis stages, converting them into quantifiable sentiment metrics that can be accurately determined automatically while preserving the nuances of both voice and text cues.
2Measurement precision
If comprehensive data processing is performed to improve sentiment analysis accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the sentiment analysis system into distinct functional modules: a text processing component that handles chat transcripts, a voice processing component that analyzes call recordings, a metadata processing component, and a sentiment determination component. Each module processes specific types of data independently before integrating results, which manages complexity while maintaining comprehensive analysis capability.
3Measurement precision
If multiple communication channels are integrated for sentiment determination, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal sentiment determination system that can process multiple communication channels through a single integrated architecture. The system is designed to handle text communications, voice communications, and metadata uniformly, using common processing frameworks and data structures that reduce integration complexity while enabling multi-channel analysis.
Data Source
AI summary
Described herein are systems and techniques to facilitate efficient determination of operator sentiment based on unstructured textual data exchanged between computing devices via communications channels. Unstructured textual data may be preprocessed to remove extraneous data and prepare the textual content for input to a machine-learned model trained to determine one or more sentiment scores based on textual data. The output of the model may be used to determine sentiment data and/or trends and to determine one or more subsequent actions.