5. Bulgaria Cross-Category Report: Relational Analysis of Key Indicator Content Volume
$50.00
This report analyzes the strategic-level relationships between the sentiment of key indicator-related online content & postings. Statistical connections, correlations, and causalities are measured & identified.
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This report analyzes the strategic-level relationships between the sentiment of key indicator-related online content & postings. Statistical connections, correlations, and causalities are measured & identified.
- Society Related Online Content & Postings – Sentiment Cross-Correlation
- Political Related Online Content & Postings – Sentiment Cross-Correlation
- General Intelligence Related Online Content & Postings – Sentiment Cross-Correlation
- Intl. Relations Related Online Content & Postings – Sentiment Cross-Correlation
- Health Related Online Content & Postings – Sentiment Cross-Correlation
- Environment Related Online Content & Postings – Sentiment Cross-Correlation
- Economic Related Online Content & Postings – Sentiment Cross-Correlation
- Military/Defense Related Online Content & Postings – Sentiment Cross-Correlation
Understanding the relationship between the various SIGINT Categories is critical in monitoring a country’s stability. These connections represent both opportunities and potential threats to the country’s prosperity and national security. With this understanding, statistical correlation analysis of key country indicators has emerged as an important strategic priority for policymakers, analysts, and other professionals assisting with national strategic initiatives.
The framework and supporting analysis contained within this report present a structured method for understanding the strategic-level relationships between the sentiment or tone of online content & postings in the SIGINT Categories. The included comparative analyses are based on compiled sentiment data sets which are processed through the TalosAI algorithm to provide a percentage Correlation/Causality score.
These scores can be compared to better understand the Category relationships and potential effects of one category on another.
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