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SKILL.md
npx skills add apidojo-io/apidojo-skills --skill tracking-brand-sentiment-across-platforms
Tracks brand sentiment across Twitter Reddit and TikTok simultaneously using apidojo's scrapers on Apify. Triggers when the user asks to: monitor brand reputation across social platforms, track how people talk about a brand on multiple channels, compare brand sentiment on Twitter vs Reddit vs TikTok, get a cross-platform brand health score, monitor a product launch reaction across social media, measure overall public sentiment for a brand, or build a multi-platform social listening dashboard for a brand. Returns per-platform sentiment distribution, cross-platform score, top positive/negative posts, and theme analysis. Ideal for brand managers, CMOs, PR teams, and reputation management agencies.
npx skills add apidojo-io/apidojo-skills --skill tracking-brand-sentiment-across-platforms
Monitors brand sentiment on Twitter, Reddit, and TikTok in parallel, then produces a unified brand health score. Each platform serves a different role: Twitter = real-time news/opinion, Reddit = deep community discussion, TikTok = Gen Z product culture.
APIFY_TOKEN environment variable set| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
searchTerms | array | ✅ | [] | Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"]) |
sort | string | Optional | Top | Sort order: Latest, Top, or Latest+Top |
tweetLanguage | string | Optional | — | ISO 639-1 language code (e.g. en) |
maxItems | number | Optional | Unlimited | Maximum tweets to return |
onlyVerifiedUsers | boolean | Optional | false | Only tweets from verified users |
onlyTwitterBlue | boolean | Optional | false | Only Twitter Blue subscribers |
onlyImage | boolean | Optional | false | Only tweets with images |
onlyVideo | boolean | Optional | false | Only tweets with videos |
onlyQuote | boolean | Optional | false | Only quote tweets |
author | string | Optional | — | Filter to a specific author handle |
inReplyTo | string | Optional | — | Tweets replying to a specific handle |
mentioning | string | Optional | — | Tweets mentioning a specific handle |
geotaggedNear | string | Optional | — | Tweets near a location |
withinRadius | string | Optional | — | Radius around geotaggedNear |
geocode | string | Optional | — | Lat/lng + radius string |
placeObjectId | string | Optional | — | Tweets tagged with a place |
minimumRetweets | number | Optional | — | Minimum retweet count |
minimumFavorites | number | Optional | — | Minimum like count |
minimumReplies | number | Optional | — | Minimum reply count |
start | string | Optional | — | Tweets after this date (YYYY-MM-DD) |
end | string | Optional | — | Tweets before this date (YYYY-MM-DD) |
includeSearchTerms | boolean | Optional | false | Add the matched search term to each tweet |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Twitter (If Apify MCP is available):
Reddit:
TikTok:
REST API fallback — run each sequentially:
Use the same lexical model for all platforms (positive/negative/neutral indicators from analyzing-twitter-sentiment-for-topic skill). Weight by platform-specific engagement:
likeCount + replyCount * 3upvotes + commentCount * 2playCount / 1000 + diggCountScore interpretation: 0–40 = Crisis, 40–55 = Concerning, 55–70 = Neutral, 70–85 = Positive, 85–100 = Strong.
ALERT — may indicate PR crisis or viral positive momentreddit_weight = 0 and redistribute to other platformsBrand health score conflicts between platforms: This is meaningful signal — discuss in output why platforms diverge (e.g. "Reddit community discusses product quality issues while TikTok shows positive unboxing content"). Sample too small for reliable sentiment (< 50 posts per platform): Widen date range or note low confidence in that platform's score. Brand name not found on a platform: Some brands have no organic TikTok presence — note as gap in output.
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
APIFY_TOKENmust be set in environment or.envfile.
Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input: {"keywords": ["#[brandname]", "#[brandname]review"], "maxItems": 200}# Twitter
curl -X POST "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{"searchTerms": ["[BRAND_NAME]"], "maxItems": 300}'
# Reddit
curl -X POST "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{"searches": ["[BRAND_NAME]"], "maxItems": 200, "sort": "new", "time": "month"}'platform_sentiment[p] = (positive_count[p] - negative_count[p]) / total_count[p] # range: -1 to +1
platform_weight = {twitter: 0.35, reddit: 0.40, tiktok: 0.25} # Reddit = most considered opinion
brand_health_score = sum(platform_sentiment[p] * platform_weight[p] for p in platforms)
brand_health_score = (brand_health_score + 1) / 2 * 100 # normalize to 0-100# Cross-Platform Brand Sentiment: [BRAND_NAME]
Period: [DATE_RANGE] | Total posts: [N] | Date: [DATE]
## Brand Health Score: [X]/100 — [INTERPRETATION]
## Per-Platform Breakdown
| Platform | Posts | Positive | Negative | Neutral | Score |
|----------|-------|----------|----------|---------|-------|
| Twitter | [N] | [X%] | [X%] | [X%] | [+/-X] |
| Reddit | [N] | [X%] | [X%] | [X%] | [+/-X] |
| TikTok | [N] | [X%] | [X%] | [X%] | [+/-X] |
## Top Negative Themes (Cross-Platform)
1. [Theme] — [N] posts across [platforms]
2. [Theme]
## Top Positive Themes
1. [Theme] — [N] posts
2. [Theme]
## Most Impactful Posts
🔴 Top negative: [platform] | [handle] | [N engagement] | "[excerpt]"
🟢 Top positive: [platform] | [handle] | [N engagement] | "[excerpt]"# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format json