Text Processing System for Automated Theme Extraction
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Solution Overview
Problem
Companies face challenges in efficiently extracting themes from large volumes of textual data related to customer service interactions, which is time-consuming and often lacks comprehensive analytics.
Innovation Solution
A text processing system that cleanses textual data, extracts phrases, clusters them into hierarchical themes, and generates a graphical representation for visualization, utilizing techniques such as language detection, spell checking, and embedding representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of customer service interactions is performed, then data analysis can be conducted, but it is extremely time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational systems including text cleansing modules, phrase extraction algorithms, clustering mechanisms, and visualization tools. This substitution transforms the time-consuming manual analysis into efficient automated processing while maintaining comprehensive data examination capabilities
Solution Approach 2:
The system enables self-service automated theme extraction and visualization generation without requiring manual intervention. The text processing system automatically cleanses data, extracts meaningful phrases, clusters them into themes, and generates visual representations, allowing the data to analyze itself rather than requiring human reviewers
2Loss of information
If comprehensive analytics are generated from textual data, then valuable insights can be obtained, but the complexity of processing and analyzing the data increases
Solution Approach 1:
The patent segments the complex analysis process into distinct modular components: text cleansing module, phrase extraction module, clustering module, and visualization module. Each module handles a specific aspect of the analysis, reducing overall system complexity while enabling comprehensive analytics through coordinated operation of these specialized components
Solution Approach 2:
The patent introduces intermediary processing layers including text cleansing that transforms raw text into standardized format, and phrase extraction that serves as a bridge between raw text and thematic clusters. These intermediaries simplify the complexity by creating structured intermediate representations that are easier to process and analyze
3Measurement precision
If text data is cleansed through multiple processing steps, then extraction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary text cleansing operations including removing non-ASCII characters, expanding contractions, removing numbers and punctuation, and lemmatizing text before phrase extraction. This preliminary processing prepares the data in advance, improving subsequent extraction accuracy while establishing a efficient processing foundation that reduces overall computational burden
Data Source
AI summary
Disclosed herein are apparatus, system, method, and computer-readable medium aspects for extracting themes from textual data. Textual data is initially cleansed from its submitted form into a simplified form for improved accuracy of topic extraction. From the cleansed text, phrases are extracted. Embeddings of the phrases are then determined so that similarities can be identified between different phrases within the text. Using these embodiments, clustering is performed on the embeddings to reveal the topics included within the text submission, as well as their frequency and relationship to one another. This clustering processing can be repeated at multiple levels of granularity for improved accuracy. Based on an analysis of the resulting clusters, a graphical representation of the clusters at the various levels is generated to provide an easy-to-understand indication of the body of text and the topics and themes included therein.