Reddit Sentiment & Trend Tracker — Mention Volume + Sentiment Over Time

Track how Reddit talks about your ticker, brand, product, or topic — with chart-ready time series. This Actor monitors mention volume and sentiment across subreddits (or site-wide), outputs per-mention rows with sentiment scores, and aggregated trend buckets you can plug straight into dashboards.

No setup. Just results. Schedule it, webhook spikes to Slack, and build living sentiment charts without heavy ML or expensive compute.

⚡ Built for speed & cost. Runs on 512 MB, streams results live, and stays cheap even on scheduled hourly runs.

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⚡ Why this Actor?

Metric	This Actor	Typical alternative
Speed	Fast, lightweight	Slow page loads
Default memory	512 MB	2–4 GB
Setup	Works out of the box	Often complex configuration
Output	Mentions + trend buckets + top content	Raw posts only
Sentiment	Built-in lexicon (finance/product presets)	Bring your own
Scheduling	Incremental cursors + saved state	Manual dedupe

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Who is this for?

You are…	You get…
Finance / crypto analyst	Ticker mention volume + bullish/bearish pulse (WSB, stocks, crypto subs)
Brand / product team	Perception tracking, complaint spikes, campaign impact
Market researcher	Topic sentiment trends over time with exportable buckets
PR / comms	Early warning when mention volume or net sentiment jumps
Developer / integrator	Clean JSON for BI tools, MCP agents, and custom dashboards

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What you get

Per-mention rows (featureType: "mention")

Every matched post or comment includes: source fields (id, title/body, url, permalink, subreddit, author, score, createdAtISO), plus term, matchedAliases, matchedSnippet, sentimentScore (-1 to 1), sentimentLabel (positive/neutral/negative), and sentimentTerms (words that drove the score).

Trend buckets (featureType: "trend_bucket")

Chart-ready time series per term + interval (hour | day | week):

• mentions, uniqueAuthors, totalUpvotes
• avgSentiment, netSentiment
• positive, neutral, negative counts
• bucketStartISO, bucketEndISO

Perfect for: Google Sheets, Looker, Grafana, Power BI, or any tool that plots (bucketStartISO, mentions, netSentiment).

Top content (featureType: "top_content")

Top N posts/comments per term ranked by score and engagement — spot the posts driving the conversation.

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Discovery modes

1. Search — find mentions by keyword across Reddit or within chosen subreddits, with sort and time filters.
2. Scan — watch recent posts and comments in target subreddits for your terms.
3. Both — combine for maximum coverage (default).

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Sentiment engine

• Default: Fast rule-based lexicon with negation handling, boosters, and emoji signals. Works offline — no third-party services required.
• Presets: general, finance (moon, calls, puts, rug, dump…), product (buggy, refund, love it…)
• Custom: Add your own word → score map via customLexicon
• Optional: Enable useLlmSentiment + openaiApiKey for higher accuracy when you need it

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How to run

1. Add keywords — one per line, e.g. Nvidia: NVDA, $NVDA or just Tesla.
2. Set subreddits (e.g. stocks, wallstreetbets) or ["all"] for site-wide.
3. Pick interval (day for daily charts).
4. Choose lexiconPreset (finance for tickers).
5. Keep Proxy → Residential US enabled on Apify.
6. Run — download results or connect a schedule + webhook.

Pro tip: Schedule every hour with interval: "hour" and webhook spikeThreshold to catch mention surges in real time.

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Example input (Apify / MCP)

{
  "termGroups": ["Nvidia: NVDA, $NVDA"],
  "mode": "both",
  "subreddits": ["stocks", "wallstreetbets"],
  "stream": "both",
  "interval": "day",
  "lexiconPreset": "finance",
  "searchTime": "week",
  "resultsPerTerm": 100,
  "lookback": 500,
  "emitMentions": true,
  "emitAggregates": true,
  "topN": 10,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}

Shorthand terms

{
  "termGroups": ["Tesla", "Apple: AAPL"],
  "subreddits": ["investing"],
  "interval": "week",
  "lexiconPreset": "finance"
}

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Example output

Mention row:

{
  "featureType": "mention",
  "streamType": "post",
  "term": "Nvidia",
  "matchedAliases": ["NVDA"],
  "matchedSnippet": "… loaded up on NVDA calls before earnings …",
  "sentimentScore": 0.42,
  "sentimentLabel": "positive",
  "sentimentTerms": ["calls"],
  "subreddit": "wallstreetbets",
  "title": "NVDA yolo",
  "score": 847,
  "createdAtISO": "2026-07-06T14:22:11.000Z",
  "permalink": "https://www.reddit.com/r/wallstreetbets/comments/..."
}

Trend bucket row:

{
  "featureType": "trend_bucket",
  "term": "Nvidia",
  "interval": "day",
  "bucketStartISO": "2026-07-06T00:00:00.000Z",
  "bucketEndISO": "2026-07-07T00:00:00.000Z",
  "mentions": 34,
  "uniqueAuthors": 28,
  "avgSentiment": 0.18,
  "netSentiment": 6.12,
  "positive": 14,
  "neutral": 12,
  "negative": 8,
  "totalUpvotes": 4200
}

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Input reference

Field	Description	Default
termGroups	Keywords to track (Label: alias1, alias2 or just Label)	—
mode	search, scan, or both	both
subreddits	Target subs; ["all"] = site-wide	["all"]
stream	posts, comments, or both	both
interval	Bucket size: hour, day, week	day
lexiconPreset	general, finance, product	general
customLexicon	Word → score overrides	{}
useLlmSentiment	Optional OpenAI sentiment	false
resultsPerTerm	Search fetch limit per term	100
lookback	Scan depth per subreddit (1–1000)	500
minScore	Minimum upvote score filter	0
topN	Top content rows per term	10
emitMentions	Output per-mention rows	true
emitAggregates	Output trend buckets	true
spikeThreshold	Webhook trigger threshold	0 (off)
webhookUrl	Spike alert destination	—
sinceId	Incremental item cursor	—
maxItems	Cap mention rows (0 = unlimited)	0

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Scheduling + incremental runs

Scheduled re-runs extend your time series without duplicates:

• Pass sinceId for a one-shot incremental cutoff, or
• Let the Actor persist newest-seen cursors per term/subreddit automatically.

Each scheduled run adds only new mentions and updates trend buckets.

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Spike webhooks

Set spikeThreshold (e.g. 25) and webhookUrl to get alerts when a term's latest bucket crosses the threshold on mention count or net sentiment magnitude.

{
  "spikeThreshold": 30,
  "webhookUrl": "https://hooks.slack.com/services/...",
  "webhookFormat": "slack"
}

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Local development

npm install
cp local.input.example.json local.input.json
npm run start:local           # writes output/local_results.json
npm run build                 # compile for Apify

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FAQ

Do I need any special setup?
No. Open the Actor, add your terms, and click Start.

Does sentiment work without extra services?
Yes. The default lexicon path runs entirely in-process. LLM mode is optional.

Can I track multiple terms at once?
Yes. Each term gets its own buckets, mentions, and top content.

How do I chart trends?
Filter dataset view trends or export CSV. Plot bucketStartISO vs mentions or netSentiment grouped by term.

What proxy should I use?
Residential US on Apify is recommended for the most reliable runs.

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Contact me

Need something built beyond this Actor? I design and ship custom data projects end to end — from one-off automations to production pipelines.

What I can help with:

• Data extraction & automation — any website, marketplace, social platform, or internal tool; scheduled runs, deduplication, clean JSON/CSV output
• Apify Actors — purpose-built actors like this one, tuned for your use case and budget
• Web apps — dashboards, admin panels, SaaS products, landing pages, and full-stack applications of any kind
• Integrations — webhooks, Slack alerts, Google Sheets, BI tools, CRMs, and custom APIs

Whether you need a custom data pipeline, a sentiment monitoring tool, or a complete web product, reach out and let's talk scope.

• Email: dubem115@gmail.com
• GitHub: github.com/DrunkCodes

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License

ISC