Topic Drift Detection in Customer Feedback Classification
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Solution Overview
Problem
Current classification and clustering algorithms fail to effectively manage large volumes of documents related to customer support issues and sentiment, often grouping new documents into existing classes unrelated to the issues, leading to unnoticed support problems or misemphasis on unrelated topics.
Innovation Solution
A computer-implemented method and system that uses trained classifiers to classify and cluster documents based on voting, with a drift engine to detect topic changes related to customer support issues or sentiment, rebuilding classifiers and re-clustering when a predetermined threshold is exceeded, and applying word analysis to accurately group documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current classification and clustering algorithms are used to manage large volumes of documents, then documents can be grouped into existing classes, but new documents related to new customer support issues are incorrectly grouped into unrelated existing classes, leading to topic drift
Solution Approach 1:
The patent implements dynamic adaptation of classification algorithms by continuously monitoring document streams for topic drift. When drift is detected (indicating new customer support issues), the system automatically updates classification models and creates new clusters, transforming static classification into a dynamic system that evolves with emerging topics while maintaining high classification accuracy
Solution Approach 2:
The system employs feedback mechanisms by monitoring classification results and detecting topic drift in real-time. This feedback loop identifies when new customer support issues emerge and triggers retraining of classification algorithms, ensuring continuous improvement of classification precision without sacrificing processing efficiency
2Stability of the object's composition
If new customer support issues are not recognized, then existing classification structures remain stable, but new issues go unnoticed and importance of unrelated topics is erroneously accentuated
Solution Approach 1:
The patent applies preliminary action by proactively monitoring document streams for emerging topics before they become dominant. The system detects topic drift early and preemptively updates classification structures, preventing loss of information about new customer support issues while maintaining overall classification stability through controlled, incremental updates
3Productivity
If classification algorithms group all documents into existing classes, then processing is efficient, but new topics cause misclassification and misemphasis of unrelated topics
Solution Approach 1:
The system dynamically adapts classification structures by detecting topic drift and automatically creating new clusters for emerging customer support issues. This dynamic approach maintains high processing speed through efficient automated detection while ensuring reliable sentiment analysis by accurately categorizing documents according to current topical structures
Data Source
AI summary
A method and system of classifying documents is provided. The method includes receiving a stream of documents from at least one user wherein each document includes a topic of information relating to a customer support issue or sentiment. The method includes classifying each of the received documents using a plurality of trained classifiers, the classification based on a voting by the trained classifiers, each document labeled according to a similar topic. A drift of the topic of one or more of the classifications is determined wherein the drift is related to the received documents that include information relating to an unclassified customer support issue or sentiment. If the determined drift exceeds a predetermined threshold range, rebuilding the plurality of classifiers to include a second set of classifiers trained to recognize the unclassified customer support issue or sentiment.