Semantic Sage-AI-powered text analysis tool

Example

Analyzing the evolution of the COVID-19 origin narrative on social media.

Scenario

CSF can be used to track how specific narratives evolve over time within different online communities. For instance, by identifying key thematic shifts in the discussion on COVID-19 origins, CSF reveals how misinformation spread, which communities were most affected, and how the focus shifted from one conspiracy theory to another. This allows researchers and policymakers to pinpoint key moments of narrative transformation and community influence.

Example

Identifying the most relevant topics in large-scale social media discussions.

Scenario

When analyzing thousands of social media posts on a trending topic, the Optimal Entropy Model helps sift through the noise and surface the most informative and diverse content. For instance, if a company is launching a new product, this function can detect which features or aspects of the product are generating the most engagement or polarization, aiding in refining marketing strategies or public relations efforts.

Example

Comparing the effectiveness of ICT policies across African nations.

Scenario

By analyzing the semantic content of policy documents from different countries, Semantic Sage can identify how specific word choices or conceptual structures in the documents relate to actual ICT outcomes. For example, comparing two countries with similar policies but different levels of technology adoption can highlight subtle semantic differences that may influence policy effectiveness, aiding governments or NGOs in crafting better policies.