LearnCurator - I built a YouTube tutorial search engine that filters videos, ranks by AI‑analysed comments

#webdev #javascript #ai #api

LearnCurator – Stop wasting time on bad YouTube tutorials

I love learning from YouTube, but I kept running into the same problems:

Shorts mixed into search results (too short to teach anything)
Non‑English/Hindi videos (I don’t speak Tamil or Telugu)
Outdated tutorials (still showing 4‑year‑old videos even if better ones exist)
No way to tell quality until I’ve watched 5 minutes

So I built LearnCurator – a smart search engine for YouTube tutorials.

🔍 How it works

Initial engagement score The app first computes a base score for each video using: -engagementRatio = likes / views (capped at 1.0) -recencyFactor based on publish age

In youtubeService.js: -score = engagementRatio * 0.7 + recencyFactor * 0.3 -So videos with high like/view ratio and recent publish date rank higher.

Sentiment and final score After getting the top candidate videos, it enriches them with sentiment analysis and a popularity boost.

Final score formula: finalScore = engagementRatio * 0.3
recencyFactor * 0.1
sentimentScore * 0.3
viewBoost * 0.3

🤖 AI‑powered comment analysis

I integrated Gemini 1.5 Flash to analyse YouTube comments. The AI counts positive vs negative feedback and adds a sentiment score to each video. This helps surface content that the community actually loved – not just clicked on.

👍👎 User feedback

Every video card has 😍 😐 😞 buttons. Users can rate a tutorial and leave optional comments. I built a private admin dashboard where I can see real‑time feedback, average ratings, and trending complaints.