Social Media Sentiment Correlation for Real-Time Computing Issue Detection
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Solution Overview
Problem
Online service providers face limited visibility into computing issues that occur outside data centers, leading to potential customer dissatisfaction, as existing systems struggle to identify and address problems in real-time.
Innovation Solution
Utilizing machine learning and artificial intelligence engines to analyze social media comments for anomaly detection, sentiment analysis, and correlation with internal signals to identify and resolve computing issues in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service providers use traditional observability systems to monitor computing issues, then system reliability is maintained within data centers, but visibility into customer-side issues is limited and response time is delayed
Solution Approach 1:
The patent extends monitoring from the traditional data center dimension to the customer-side social media dimension. By collecting and analyzing social media comments about service issues, the system gains visibility into customer-side problems that traditional observability systems cannot detect, enabling earlier issue detection and faster response times while maintaining service reliability.
2Loss of information
If service providers expand monitoring to external social media platforms, then visibility into customer-side issues improves, but system complexity increases
Solution Approach 1:
The patent introduces social media platforms as intermediary sources for collecting customer feedback. Instead of directly monitoring customer devices or networks, the system collects publicly available social media comments as an intermediary data source, which provides customer-side issue visibility without requiring direct integration into customer infrastructure, thereby limiting the increase in system complexity.
Solution Approach 2:
The patent replaces traditional mechanical monitoring systems that directly probe customer infrastructure with an information-based system that analyzes social media text data. This substitution uses natural language processing and sentiment analysis algorithms instead of direct system probing, reducing the complexity of external monitoring while improving customer issue visibility.
3Productivity
If real-time analysis of social media comments is implemented, then issue response speed improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by selectively analyzing only relevant social media comments that indicate potential service issues. Instead of processing all social media data, the system uses filtering mechanisms to identify and analyze only comments related to service problems, enabling real-time issue detection with reduced computational resource consumption while maintaining fast response speeds.
Data Source
AI summary
Methods and systems described herein may implement operations for identification of computing issues on computing platforms using social media comments and other available data in a variety of environments. An online transaction processor may provide operations for electronic transaction processing and/or other online computing services. The online transaction processor may monitor social media posts in order to determine if context and sentiments from such posts may indicate that there is a potential issue or complaint by users with computing services provided by the online service provider. This may be done by processing the posts using a machine learning engine for sentiment analysis and correlating sentiments with corresponding computing signals occurring with computing platforms of the service provider. Thereafter, computing anomalies may be detected and output notifications may be provided to users based on the corresponding computing anomalies.