Automated Complaint Detection Using Sentiment Metrics
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Solution Overview
Problem
Current methods for identifying complaints in customer interactions, such as those in call centers, are time-consuming and subjective, relying on human agents and resulting in inconsistent and low-accuracy categorization.
Innovation Solution
A computer-based system and method that detects linguistic structures related to complaints, calculates sentiment metrics, and classifies interactions using a trained supervised learning model, such as a support vector machine (SVM) classifier, to automate the complaint detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human agents manually review interactions to identify complaints, then the process can be performed with simple tools, but it is time-consuming and has low accuracy
Solution Approach 1:
The patent replaces the mechanical system of human agents manually reviewing interactions with an automated computer-based system that uses natural language processing, sentiment analysis, and machine learning models to detect complaints. This substitution eliminates manual labor while improving both speed and accuracy through automated linguistic structure detection and sentiment metric calculation.
Solution Approach 2:
The system enables interactions to self-analyze for complaint detection by automatically processing the interaction data, detecting linguistic structures, calculating sentiment metrics, and classifying complaints without requiring human intervention. The automated system serves itself by performing the entire complaint detection workflow autonomously.
2Reliability
If human agents categorize interactions, then the process is flexible and adaptable, but it is subjective and inconsistent
Solution Approach 1:
The patent transforms the subjective human judgment process into an objective automated classification system by changing the parameters from human opinion to quantifiable metrics. The system uses objective linguistic structure detection, sentiment score calculations, and machine learning model outputs to consistently classify interactions, eliminating subjectivity while maintaining reliability across different users.
Solution Approach 2:
The system creates a standardized copy of the complaint detection process through automated algorithms and machine learning models that replicate the analysis consistently across all interactions. Instead of relying on individual human agents' unique judgment styles, the system copies the same analytical framework and classification rules for every interaction, ensuring uniformity and consistency.
3Productivity
If automated analysis is implemented, then productivity increases, but the system complexity increases
Solution Approach 1:
The patent segments the automated complaint detection system into distinct functional modules: interaction data reception, linguistic structure detection, sentiment analysis, machine learning classification, and result output. This segmentation allows each component to be independently developed, tested, and optimized, managing overall system complexity while enabling high productivity through parallel processing of multiple interactions.
Data Source
AI summary
A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.