Social Intelligence System Using Message Queues for Data Scoring
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Solution Overview
Problem
Current sentiment analysis systems inefficiently process and analyze large volumes of social media data, requiring extensive storage in relational databases before analysis can be performed, making it difficult to quickly determine sentiment for specific individuals or entities.
Innovation Solution
A social intelligence system that streams social media data into message queues, scores it using predefined factors, and stores the analyzed data in an analysis database, allowing for efficient processing and analysis without the need for initial relational database storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If social media data is stored in a relational database before analysis, then data can be organized and retrieved, but processing efficiency is reduced and analysis time is increased
Solution Approach 1:
The patent applies preliminary action by scoring social media data segments with sentiment scores before storing them in the database. The system performs sentiment analysis on incoming data streams and assigns scores (positive, negative, neutral) to each segment prior to storage. This allows the database to store pre-processed, scored data that can be quickly retrieved and aggregated for analysis without requiring re-processing at query time, thus improving productivity and reducing analysis time.
Solution Approach 2:
The patent segments the data processing workflow into distinct stages: data reception, sentiment scoring, and database storage. By dividing the large volume of social media data into individual data segments that are processed and scored independently, the system can efficiently handle high-volume streams without overwhelming the database. Each segment is scored separately and stored with its score, enabling rapid aggregation and analysis later.
2Reliability
If mass volumes of social media data are collected and stored before analysis, then comprehensive sentiment analysis can be performed, but system complexity and storage requirements increase
Solution Approach 1:
The system performs preliminary sentiment scoring on each data segment as it arrives, assigning a sentiment score (positive, negative, or neutral) before storage. This preliminary action ensures that when data is retrieved from the database, the sentiment analysis has already been performed, maintaining reliability of the analysis while avoiding the need for complex real-time processing systems during data retrieval and aggregation.
3Ease of operation
If data is stored in a relational database, then data can be organized for retrieval, but processing speed is reduced
Solution Approach 1:
The patent applies preliminary action by scoring social media data segments with sentiment scores before storing them in the database. The system performs sentiment analysis on incoming data streams and assigns scores (positive, negative, neutral) to each segment prior to storage. This allows the database to store pre-processed, scored data that can be quickly retrieved and aggregated for analysis without requiring re-processing at query time, thus improving productivity and reducing analysis time.
Data Source
AI summary
A social intelligence system is presented that streams information from a source, queues the streamed information, analyzes/scores the queued data, and stores the analyzed/scored data in an analysis database. The analyzed/scored data can then be retrieved from the database for post-processing and stored in a client specific database for further reporting. By streaming the data into various message queues and scoring the data before storing in the analysis database, large volumes of data can be efficiently processed and analyzed for a particular person and/or entity.