Anomaly Detection System Using Social Media Sentiment for Alert Prioritization
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Solution Overview
Problem
Complex computing environments face challenges in detecting and diagnosing issues due to overwhelming alerts, with difficulty in determining alert significance and prioritization, as existing monitoring systems struggle to identify and address critical problems efficiently.
Innovation Solution
Anomaly detection systems utilize social media messages to identify issues and events by aggregating messages based on timestamps, performing sentiment analysis, and generating alerts with priority levels, enabling quicker issue resolution and improved system efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex monitoring and alerting systems are built to detect issues in computing environments, then the ability to monitor system status is improved, but the difficulty of identifying and diagnosing underlying issues increases due to overwhelming numbers of alerts
Solution Approach 1:
The patent introduces social media sentiment data as an intermediary layer between traditional monitoring systems and issue diagnosis. By aggregating and analyzing user-reported problems from social media platforms, the system creates a new information channel that helps prioritize and contextualize technical alerts, making the overall system less complex to interpret
Solution Approach 2:
The system implements feedback loops where social media sentiment analysis results feed back into the alert prioritization mechanism. User complaints and sentiment scores continuously inform which alerts require immediate attention, creating a dynamic prioritization system that adapts to actual user impact rather than relying solely on technical severity metrics
2Reliability
If traditional alerting systems monitor computing environments, then system issues can be detected, but the ability to prioritize which alerts to address first deteriorates due to lack of context about user impact
Solution Approach 1:
The system performs preliminary analysis of social media sentiment data before alerts require human intervention. By pre-aggregating user complaints and calculating sentiment scores, the system prepares prioritization information in advance, so when alerts need attention, the ranking is already determined based on user impact rather than requiring time-consuming analysis
Solution Approach 2:
The patent replaces manual prioritization processes with automated sentiment analysis mechanisms. Instead of engineers manually assessing alert priority based on limited information, machine learning models automatically analyze social media data to determine user impact and generate prioritized alert rankings, dramatically reducing the time required to identify critical issues
3Loss of information
If enterprises use social media monitoring to gather user feedback, then understanding user sentiment is improved, but the complexity of integrating this data with existing monitoring systems increases
Solution Approach 1:
The patent creates a multi-functional platform that simultaneously performs social media data collection, sentiment analysis, alert prioritization, and incident detection. By consolidating these functions into a unified system with standardized data interfaces, the complexity of integrating multiple separate tools is reduced, and the system can handle various data sources and output formats through common mechanisms
Data Source
AI summary
Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.