Reddit & Forum Sentiment Analyzer

Mine authentic brand, product, and competitor sentiment from Reddit and Hacker News at scale. Built for product managers running user research, brand managers monitoring reputation, marketing agencies benchmarking clients against competitors, startups validating pre-launch demand, and investors doing market due diligence.

Reddit has 70M+ daily active users and is the most unfiltered source of authentic product opinion on the internet. This actor turns a single brand or product keyword into a structured sentiment report: per-post classification, theme clusters, competitor comparison, and trend over time — pay per post analyzed.

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What you get per post

Each Reddit or Hacker News post that matches your keyword is parsed into one structured record:

{
  "recordType": "post",
  "keyword": "Notion",
  "source": "reddit",
  "scope": "r/productivity",
  "id": "1ab2cd3",
  "permalink": "https://www.reddit.com/r/productivity/comments/1ab2cd3/...",
  "title": "Switched from Notion to Obsidian — best decision",
  "author": "user42",
  "score": 487,
  "upvoteRatio": 0.94,
  "commentCount": 156,
  "createdAt": "2026-05-04T14:22:11.000Z",
  "sentiment": {
    "post": { "score": -0.42, "positive": 1, "negative": 3, "label": "negative", "confidence": 0.4 },
    "comments": { "positive": 4, "negative": 6, "neutral": 5 },
    "overall": { "score": -0.23, "positive": 7, "negative": 11, "label": "negative", "confidence": 1 }
  },
  "topics": ["alternatives", "performance", "pricing"],
  "competitorsMentioned": ["Obsidian"],
  "isComparativePost": true,
  "commentsAnalyzed": 15,
  "topComments": [
    { "author": "user99", "score": 78, "preview": "I left Notion last year. It's slow and the pricing got nuts...", "sentiment": "negative" }
  ]
}

And one rolled-up recordType: "summary" record per keyword:

{
  "recordType": "summary",
  "keyword": "Notion",
  "postCount": 48,
  "sentimentBreakdown": { "positive": 21, "neutral": 14, "negative": 13 },
  "averageSentimentScore": 0.08,
  "topThemes": [
    { "theme": "features", "count": 27 },
    { "theme": "performance", "count": 19 },
    { "theme": "pricing", "count": 15 },
    { "theme": "alternatives", "count": 11 }
  ],
  "competitorMentions": { "Obsidian": 18, "Roam": 6 },
  "competitorComparativeSentiment": {
    "Obsidian": { "mentions": 18, "comparativeAvgSentiment": -0.11, "breakdown": { "positive": 5, "neutral": 6, "negative": 7 } }
  },
  "trendByDay": [
    { "day": "2026-05-10", "posts": 8, "avgSentiment": 0.12 },
    { "day": "2026-05-11", "posts": 11, "avgSentiment": -0.04 }
  ],
  "sources": { "reddit": 39, "hackernews": 9 }
}

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Input

{
  "keywords": ["Notion"],
  "competitors": ["Obsidian", "Roam"],
  "subreddits": ["productivity", "notion"],
  "sources": ["reddit", "hackernews"],
  "maxPosts": 50,
  "maxCommentsPerPost": 10,
  "dateRange": "month",
  "sortBy": "relevance"
}

• keywords (required) — one or more brand, product, or topic terms. Each is searched and summarised separately.
• subreddits — restrict Reddit to these subs. Leave empty for site-wide search.
• sources — reddit, hackernews, or both.
• maxPosts — cap per keyword. Each post is one billable record.
• maxCommentsPerPost — top comments fetched per post and folded into the sentiment score. 0 skips comment fetch entirely (faster, cheaper, but less accurate).
• dateRange — hour, day, week, month, year, all.
• sortBy — relevance (default, best for brand monitoring), new (best for alerts), top (best for impact), hot, comments.
• competitors — track these names in the same posts and compute comparative sentiment.
• minScore — skip posts below this upvote/point threshold.
• includeRawText — turn on to emit full post body + every comment body in each record (off by default for compact JSON).

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Sentiment & topics

Sentiment is computed with a deterministic, transparent lexicon (no opaque AI calls — fast, cheap, auditable):

• Per token, count positive vs. negative cues; negators (not, never, no, etc.) flip the sign.
• Score = (positive − negative) / (positive + negative), clipped to [-1, 1].
• Label = positive, negative, or neutral, with a confidence score [0–1].

Topics are classified into:

• pricing, features, performance, reliability, ux, support, integrations, security, mobile, collaboration, alternatives

Multiple topics per post is normal — Reddit threads rarely stay on one theme.

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Use cases

• Brand health snapshot. Run ["MyBrand"] weekly on dateRange: "week" → trend dashboard.
• Competitive intelligence. Pass your competitors in competitors; the actor flags every post where your keyword is compared against them and reports comparative sentiment.
• Pre-launch demand validation. Search a problem space (e.g. "AI meeting notes") and read the topThemes and topComments for unmet needs.
• Investor due diligence. Mine sentiment for a target company across r/startups, r/SaaS, and HN before a check.
• Show-HN tracking. Set sources: ["hackernews"] and dateRange: "week" to catch new launches.

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Pricing

Pay per post analyzed (post-analyzed event). Summary records are emitted for free. Comments are fetched and analysed in-place — no separate charge.

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Output destinations

• Default dataset: every record (post and summary) is pushed there.
• Key-value store SUMMARY: run-level counts and per-keyword breakdown.

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Notes

• Reddit data is fetched from the public *.json endpoints. No login required.
• Hacker News data is from the public Algolia search API (hn.algolia.com).
• Apify proxy is used by default for rotation; residential is not required for these endpoints in normal volumes.
• The sentiment lexicon is intentionally simple and visible inside src/main.js so you can extend it for your industry.

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Author

Johnson AI Consulting — production data and intelligence actors on the Apify marketplace.