Text Classification System Using LDA Topic Vectors
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Solution Overview
Problem
The sheer volume of textual data on the internet and in offline storage makes it difficult for users to efficiently find and classify relevant information within a reasonable time.
Innovation Solution
A computer-implemented method and system that identifies and categorizes textual data within documents by analyzing the content using techniques such as latent dirichlet allocation (LDA) and topic vectors, allowing for the return of identified categories associated with a predetermined subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of textual data is performed, then classification accuracy can be maintained, but the time required increases significantly
Solution Approach 1:
The system enables automated self-service classification of textual data using machine learning models. The classifier automatically analyzes and categorizes documents without human intervention, allowing the system to serve itself in the classification task while maintaining acceptable accuracy levels and significantly reducing time requirements.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational systems. Machine learning algorithms and natural language processing techniques substitute human analysts, transforming the classification task from a manual cognitive process to an automated computational process that operates faster and scales more efficiently.
2Productivity
If automated classification systems are implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The automated classification system is divided into distinct modular components including text preprocessing modules, feature extraction modules, machine learning classification modules, and result output modules. Each component performs a specific function independently, making the overall complex system manageable through segmentation into smaller, well-defined subsystems.
Solution Approach 2:
The system introduces intermediary processing layers between raw input and final classification output. These intermediaries include text normalization layers, feature vector transformations, and probability thresholding mechanisms that mediate between the complex machine learning models and the simple classification output, managing complexity through layered abstraction.
3Measurement precision
If comprehensive text analysis is performed on all documents, then classification accuracy improves, but computational resources required increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on the most discriminative features and keywords rather than analyzing every aspect of each document equally. The machine learning model identifies and processes only the critical portions of text that are most relevant to classification, performing excessive analysis only where necessary to achieve accurate differentiation between categories.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as text depth, feature extraction intensity, and model complexity based on document characteristics and classification confidence requirements. By changing parameters adaptively, the system maintains high classification accuracy while optimizing computational resource utilization to avoid unnecessary processing overhead.
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying a plurality of documents associated with a predetermined subject, where each of the plurality of documents contains textual data, analyzing the textual data of each of the plurality of documents to identify one or more categories within the plurality of the documents, and returning the one or more categories identified within the plurality of the documents.