Risk Event Identification System Using Frequency-Based Clustering
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Solution Overview
Problem
The exponential growth of data from various sources like social media and websites poses a challenge in accurately identifying and analyzing risk events due to the vast and diverse nature of the information, with existing sentimental analysis methods being prone to errors and inefficiencies.
Innovation Solution
A processor-implemented method and system that receives key phrases, preprocesses them, clusters based on word frequency, classifies into categories, groups entities, assigns risk scores, and analyzes high-risk events using simulation and optimization techniques to identify and rank events effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentimental analysis is used to analyze data from social media and websites, then understanding of sentiment and effects can be obtained, but accuracy is insufficient and errors are prone to occur
Solution Approach 1:
The analysis process is segmented into multiple distinct stages: key phrase extraction, pre-processing, clustering based on word frequency, classification by polarity score, grouping by frequency of incoming content stream, and risk scoring. This segmentation allows each stage to specialize in specific tasks, improving overall accuracy and reducing errors compared to monolithic sentimental analysis approaches.
Solution Approach 2:
The system changes parameters dynamically throughout the analysis process - using frequency of incoming words for clustering, polarity score for classification, frequency of incoming content stream for grouping, and multiple ranking rules for risk scoring. These parameter changes enable the system to adapt to different data patterns and improve measurement precision while maintaining reliability through multiple validation checkpoints.
2Quantity of substance
If data from multiple sources is collected and analyzed, then comprehensive risk identification is achieved, but time and effort for analysis increases exponentially
Solution Approach 1:
The system performs preliminary actions by extracting key phrases from data sources before full analysis, pre-processing the extracted phrases, and clustering them based on word frequency. These preliminary steps organize the data structure beforehand, enabling faster subsequent classification and risk assessment without requiring re-analysis of the entire dataset.
Solution Approach 2:
The system extracts only the most relevant information - key phrases from the vast data volume - and focuses analysis on these extracted elements rather than processing all raw data. This extraction approach maintains comprehensive risk identification capability while significantly reducing the time and computational effort required for analysis.
3Measurement precision
If comprehensive data processing is performed to improve analysis accuracy, then risk event identification improves, but system complexity increases
Solution Approach 1:
The complex data processing system is segmented into modular functional components: key phrase extraction module, pre-processing module, clustering module, classification module, grouping module, and risk scoring module. Each module is independent and can be developed, tested, and maintained separately, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The system introduces intermediary structures such as clustered datasets, grouped entities, and risk score rankings that mediate between raw data input and final risk assessment output. These intermediaries simplify the data transformation process and make the system more manageable by breaking down complex relationships into structured intermediate representations.
Data Source
AI summary
Conventional methods of analyzing social media content involves performing sentimental analysis to understand related sentiment and effects of events on communities. However, such analysis may not be completely accurate and are prone to errors. Present disclosure provides system and method that identify and analyze risk events from data collected from various sources. Key phrases obtained from sources is received, pre-processed, and clustered accordingly. The clustering is performed based on frequency of incoming words. The clustered dataset obtained is classified into one or more categories based on a polarity score. Dataset of specific category (e.g., negative category dataset) is analysed to identify events and topics which are then grouped using an associated label to obtain grouped entities. Each entity is then ranked and assigned a risk score for identifying high-risk events which are then analyzed using simulation and optimization technique(s) and an explainability text for the analyzed risk events is generated.