In the video, you'll watch our co-founder Kyle give a demo on how Siftree uses AI agents to analyze social media.

With AI agents, we can get survey-grade reports over observational data. This means we can use AI to classify posts as "pro" or "anti" certain viewpoints, which is much more accurate than analyzing keywords and using sentiment to guide our understanding.

Why is it better? Sentiment analysis is incredibly misleading.

Traffic-light metrics often have no idea which side a post is actually on. They measure tone, not intent, and the two collide far more often than most teams realize.

“I’m so happy the privacy-invading ad-tech industry is finally getting regulated.”

Sentiment polarity = Positive.
Actual stance toward ad-tech = Negative.

Large studies now treat this as a core failure mode and propose separate stance-detection pipelines because sentiment alone can’t recover the author’s position.

Agents can read, decipher, and classify conversations at scale much better than traditional sentiment classification models. They can categorize posts into perspectives and narratives, that actually enable us to understand positions on specific issues. A 10x improvement from sentiment analysis.