Virly uses patented StyleMatch™ technology to enable style mimicry, specifically through three dimensions of in-depth analysis:
1. Linguistic fingerprinting::
- Analyzing sentence length preferences in historical posts (short fast-paced sentences/long professional sentences)
- Testing for rhetorical features (use of prose/questions/data argumentation)
- Emotional disposition scores (formality, sense of humor, motivational, etc.)
2. Content theme modeling::
- Extract high-frequency industry terms (e.g., "ARR", "churn rate" in SaaS).
- Building a Knowledge Graph of Specialized Areas
- Identify the 3-5 core topics most frequently discussed by users
3. Interactive model learning::
- Statistical CTA (call to action) types (link clicks/comment interactions/private message leads)
- Analyzing optimal release time patterns
- Document the typical way users and fans respond
After generation, two checks are performed: first, a style similarity score (threshold ≥ 0.78) is calculated using the BERT model, and then a "rewrite" button is provided for manual fine-tuning. Users can also manually enhance specific style elements via the settings panel, for example:
- Increase/decrease in terminology density
- Adjustment of the humor index (scale 1-5)
- Set case share (corporate stories/personal experiences)
This answer comes from the articleVirly: a tool to automate LinkedIn viral content generationThe
May not be reproduced without permission:AI productivity tools " How does Virly-generated content ensure that it matches the user's personal style?
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