The tool's AI labeling system uses a two-stage processing model:
Core Technology Architecture
- content parsing layer: Analyze news text through GPT-3.5/Gemini Pro to extract features such as entities, emotional tendencies, etc.
- Label Generation Layer: Combine TF-IDF algorithm to identify high-frequency keywords and generate topic labels (e.g., "Artificial Intelligence # Technology").
- contextualization: Build semantic networks between tags (e.g., "Musk → Tesla → electric cars")
Custom Configuration Items
- pass (a bill or inspection etc)
FFUN_LIBRARIAN_TAG_PROCESSORS_CONFIGAdjustable:- Label Generation Threshold
- Blacklist/Whitelist Glossary
- multilingual processing strategy
- Rules engine support:
- Boolean logic combinations (AND/OR/NOT)
- Dynamic adjustment of weights
- time decay factor
Tests show that the default configuration of the English news labeling accuracy of 82%, Chinese need additional training word vector model to enhance the effect.
This answer comes from the articleFeeds.Fun: RSS feeds with automatic tagging and filtering of newsThe
May not be reproduced without permission:AI productivity tools " How does Feeds.Fun's AI tagging system work? What customization settings are supported?
