HN / Reddit Sentiment Analyzer — Community Mood, Structured

Pull posts from Hacker News and any subreddit, score each one with VADER sentiment analysis, and get back one clean JSON item per post — sentiment label, numeric scores, top keywords, and engagement. Perfect for tracking how a technology, product, or topic is being received across the two most influential tech communities.

Why this Actor?

• Two sources, one schema — Hacker News (Firebase API) and Reddit, normalised into identical records.
• Real sentiment, not guesses — VADER gives calibrated compound, positive, negative, and neutral scores tuned for social/short-form text.
• Filter at the source — return only positive/negative/neutral posts, or only those above a minimum upvote count.
• Keyword extraction — top terms per post so you can pivot straight into topic analysis.

Who is this for?

• Brand & product teams — monitor how your launch, feature, or company is being discussed.
• Founders & PMs — read the room on a technology before betting on it.
• Market & trend researchers — quantify community mood over time from a scheduled run.
• Content curators & newsletters — surface the most positive (or most heated) threads automatically.

How it works

1. Fetches top/new HN stories and/or hot/new/top/rising posts from the subreddits you list.
2. Runs VADER on each title (and body text where available).
3. Extracts keywords and applies your sentiment / minimum-score filters.
4. Writes one dataset item per post.

Input

{
  "sources": "both",
  "subreddit": "technology, programming",
  "sortBy": "hot",
  "hnSort": "top",
  "maxPosts": 100,
  "sentimentFilter": "",
  "minScore": 10
}

Option	Description
sources	hackernews, reddit, or both
subreddit	Comma-separated subreddits (required when sources includes reddit)
sortBy	Reddit sort: hot, new, top, rising
hnSort	HN sort: top or new
maxPosts	Max posts to analyse (default 50, up to 500)
sentimentFilter	Return only positive, negative, or neutral posts
minScore	Minimum upvotes required

Output (one item per post)

{
  "source": "reddit",
  "title": "Our team switched to Rust and shipped 40% fewer bugs",
  "author": "somedev",
  "postedAt": "2026-07-18T14:22:05.000Z",
  "sentiment": "positive",
  "compound": 0.6486,
  "positive": 0.4521,
  "neutral": 0.5479,
  "negative": 0.0,
  "score": 1250,
  "keywords": ["rust", "team", "bugs", "shipped"],
  "analysedAt": "2026-07-19T20:00:00.000Z"
}

Run it on a schedule or from your app

curl -X POST "https://api.apify.com/v2/acts/darknezz~sentiment-analyzer/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "sources": "reddit", "subreddit": "startups", "sortBy": "new", "maxPosts": 100 }'

Schedule a daily run in the Apify Console and read new items straight from the dataset to build a sentiment time series.

Pricing

Pay per event — a small fee per post analysed. No subscription: scan 20 posts or 500 and pay only for what you process.

FAQ

What sentiment model is used? VADER (Valence Aware Dictionary and sEntiment Reasoner), which is purpose-built for social-media and short-form text and returns a normalised compound score from -1 (most negative) to +1 (most positive).

Do I need Reddit or HN API keys? No. It uses the public Hacker News Firebase API and Reddit's public JSON endpoints. Enable the Apify proxy (default) to avoid rate limits on large runs.

Can I track sentiment over time? Yes — schedule the Actor and each run appends fresh, timestamped items (analysedAt) to the dataset, ready to chart as a trend.

How are keywords chosen? The top content terms per post after stop-word removal, so you can group and filter posts by topic without extra processing.