Neural Network Entity News Classification for Organizational Health Analysis
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Solution Overview
Problem
Current text classification techniques fail to effectively capture semantic relationships between organizations and other entities, particularly in predicting future trends and services needed, due to their inability to analyze soft, unstructured data comprehensively and compare organizations across different dimensions.
Innovation Solution
A system that utilizes a neural network-based entity news classification system to process and classify text into categories, providing a holistic health score for organizations by analyzing both structured and unstructured data, predicting future trends, and presenting this information visually to users for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text classification techniques are used to analyze organizational data, then the analysis process is simple and fast, but the system cannot capture semantic relationships between organizations and other entities, leading to incomplete understanding of organizational health and future trends
Solution Approach 1:
The patent introduces an intermediary layer between traditional text classification and semantic relationship extraction. This intermediary uses trained classifiers to identify event types, entities, and relationships in text, transforming unstructured text into structured relationship data that can be analyzed for organizational health trends
Solution Approach 2:
The patent replaces manual semantic analysis with automated machine learning classifiers. These classifiers are trained to recognize patterns in text and automatically extract semantic relationships, substituting human cognitive processes with computational algorithms that can process large volumes of data efficiently
2Loss of information
If comprehensive soft data and hard data are collected and analyzed to understand organizational health, then the understanding depth increases, but the time and resources required to process and understand all texts increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training classifiers on large datasets before deployment. The system pre-processes and structures data relationships in advance, creating reusable models that can quickly analyze new organizational data without requiring time-consuming manual analysis each time
Solution Approach 2:
The patent transforms unstructured text data into structured parameters representing organizational health dimensions. By changing the state of data from unstructured text to structured relationship parameters, the system enables efficient computational analysis while preserving comprehensive information about organizational contexts
3Measurement precision
If manual analysis and context organization of publications are performed, then detailed understanding is achieved, but the process is too complex and time-consuming, leading to ill-informed inferences
Solution Approach 1:
The patent implements self-service through automated classification and analysis systems. The trained classifiers independently process text data, extract relationships, and generate organizational health assessments without requiring manual intervention for each analysis task, enabling high throughput while maintaining precision
4Adaptability or versatility
If multiple organizations and entities are monitored and compared, then comprehensive competitive intelligence is obtained, but the complexity of managing and comparing data across multiple entities increases
Solution Approach 1:
The patent creates a universal analysis framework that can process and compare data across multiple organizations using the same structured relationship parameters. The system maintains standardized schemas for entities, relationships, and health dimensions that enable consistent comparison across diverse organizational contexts without requiring separate analysis systems for each entity
Data Source
AI summary
Techniques related to a system for news classification comprising one or more non-transitory memory devices and one or more hardware processors configured to execute instructions from the one or more non-transitory memory devices to cause the system to receive an article, the article including text, extract text from the received article, store the extracted text in a database, determine a set of potential target entities based on the extracted text, determine a classification of the article for each potential target entity of the set of potential target entities for a category, valence, presence of litigation, rumor, or opinion based on the extracted text, associate the classification of the article, along with a probability of the determined classification of the article for each potential target entity, assign the classification of the article if the probability of the classification is greater than a threshold probability, and store the classification of the article and the probability.