Can LLMs extract audience traits better than comment similarity?

Content creators struggle to understand their audience beyond surface metrics. YouTube Studio provides demographics and retention rates but not the depth needed for content decisions. Comments tend toward emotional reactions and surface-level feedback rather than expressing deeper motivations and needs.

Proxona (2024) introduces a dimension-value framework where LLMs analyze comments to extract latent audience characteristics. Dimensions are broad personal characteristic categories (hobbies, expertise levels, learning styles). Values are specific attributes within dimensions (basketball, novice, experiential). The pipeline generates audience observation summaries per video, combines them with transcript summaries, then extracts channel-level dimensions and values.

The key comparison: clustering comments by dimension-value associations produces more homogeneous groups than conventional k-means clustering on comment text alone. Semantic similarity of comments captures what people say; dimension-value extraction captures what kind of person says it. This is the difference between topic clustering and psychographic segmentation.

Creators then converse with synthetic personas constructed from these clusters, soliciting feedback and testing content ideas. The personas serve as proxies, not replicas — the goal is effective targeting, not exact replication. This connects to Can AI-generated personas build genuine empathy in product teams? in that both systems generate useful cognitive models of audiences but face limits on emotional depth.

A notable finding: persona consistency in conversations was mixed, with some participants observing repeated keywords and wanting more "humanness" and "caprice" — suggesting that even well-grounded personas suffer from the regularity artifacts of LLM generation.