Should we implement a supervised classifier, rule-based filters, or hybrid approach — and what datasets are needed? #3

machenxi · 2025-09-28T19:52:48Z

machenxi
Sep 28, 2025
Maintainer

Should we implement a supervised classifier, rule-based filters, or hybrid approach — and what datasets are needed?

Answered by lorine93s

Classifier choice: supervised, rule-based, or hybrid

Rule-based: simple, fast to implement (keywords, hashtags, author reputation). Works well for clear patterns.
Supervised ML classifier: learns complex patterns, better at nuance, but requires labeled dataset of tweets → “token-launch” vs “non-token” signals.
Hybrid (recommended): rules filter obvious non-signals first, then ML scores remaining tweets. This reduces noise and improves reliability.
Datasets needed: historical token launch tweets, labeled for success/failure; negative samples (noise) to reduce false positives; optionally augment with features like user reputation, linked contract addresses, timestamps.

lorine93s · 2025-09-28T19:53:27Z

Classifier choice: supervised, rule-based, or hybrid

Rule-based: simple, fast to implement (keywords, hashtags, author reputation). Works well for clear patterns.
Supervised ML classifier: learns complex patterns, better at nuance, but requires labeled dataset of tweets → “token-launch” vs “non-token” signals.
Hybrid (recommended): rules filter obvious non-signals first, then ML scores remaining tweets. This reduces noise and improves reliability.
Datasets needed: historical token launch tweets, labeled for success/failure; negative samples (noise) to reduce false positives; optionally augment with features like user reputation, linked contract addresses, timestamps.

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