Textual Data Analysis System Using Automated NLP Framework Selection
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Solution Overview
Problem
Current systems for analyzing and predicting the impact of textual data are inefficient due to high dependency on manual tasks, slow processing, and the need for user correlation with key performance indicators, especially when dealing with unstructured natural language data from various sources.
Innovation Solution
A system and method that includes a processing subsystem with a natural language processing (NLP) module for identifying contexts, applying feature engineering, and using machine learning models to predict future values, reducing manual intervention by automatically extracting data from multiple sources through web crawling and matching with appropriate NLP frameworks based on parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tasks are used to analyze and predict textual data, then the system can handle complex analysis requirements, but the processing speed decreases and efficiency is lowered
Solution Approach 1:
The system performs self-service by automatically selecting appropriate NLP frameworks and analysis methods based on the characteristics of the textual data, eliminating the need for manual task assignment while maintaining analysis accuracy through automated framework selection
Solution Approach 2:
The system dynamically adapts its analysis approach by selecting different NLP frameworks and methods based on the specific characteristics of the input data, allowing it to optimize between accuracy and processing speed for different data types and contexts
2Adaptability or versatility
If special instructions are required to analyze and predict data, then the system can perform specialized analysis, but the dependency on manual tasks increases and efficiency decreases
Solution Approach 1:
The system automatically selects and configures appropriate NLP frameworks and analysis methods based on the characteristics of the textual data, eliminating the need for users to provide special instructions while maintaining specialized analysis capabilities
Solution Approach 2:
The system performs preliminary analysis of the textual data to automatically determine the most suitable NLP framework and analysis method before executing the analysis, thereby eliminating the need for manual instruction configuration
3Loss of information
If user correlation with key performance indicators is required, then the system can provide insightful analysis, but the system complexity increases and processing is delayed
Solution Approach 1:
The system automatically correlates analysis results with relevant key performance indicators by selecting appropriate frameworks and methods, eliminating the need for manual user correlation while maintaining insight quality through automated framework selection
Solution Approach 2:
The system incorporates automated feedback mechanisms that correlate analysis results with key performance indicators based on the selected NLP framework, reducing system complexity by automating the correlation process while preserving insight quality
4Quantity of substance
If multiple data sources are processed, then the system can analyze comprehensive data, but the processing time increases and efficiency decreases
Solution Approach 1:
The system segments the processing of multiple data sources by selecting and applying specialized NLP frameworks for different types of textual data, allowing parallel processing of diverse data sources while maintaining comprehensive analysis coverage
Solution Approach 2:
The system dynamically selects and applies different NLP frameworks optimized for specific data sources and types, enabling efficient parallel processing of multiple data sources while maintaining comprehensive analysis capability
Data Source
AI summary
System and method to analyze and predict impact of textual data are provided. The system also includes a processing subsystem configured to select textual data from a plurality of data sets stored in a memory, to extract data from external sources using crawling, to identify at least one context of the textual data using one or more identification methods. The processing subsystem includes an NLP module configured to match the textual data with NLP frameworks using a mapping method based on a plurality of parameters, to apply feature engineering and transformation on the textual data to extract a plurality of features from the plurality of data sets and to analyze matched textual data of the textual using at least one analysis method. The processing subsystem also includes a predictive module configured to predict one or more future values of the analyzed textual data using the one or more predictive methods.