Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill tracking-brand-sentiment-across-platformsPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
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.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| 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 |
Workflow
Progress:
- [ ] Step 1: Run scrapers for all three platforms in parallel
- [ ] Step 2: Classify sentiment per platform
- [ ] Step 3: Calculate cross-platform brand health score
- [ ] Step 4: Identify top themes and alerts
- [ ] Step 5: Deliver unified reportStep 1: Run Three Scrapers
Twitter (If Apify MCP is available):
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input: {"searchTerms": ["[BRAND_NAME]"], "maxItems": 300, "tweetLanguage": "en"}Reddit:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input: {"searches": ["[BRAND_NAME]"], "maxItems": 200, "sort": "new", "time": "month"}TikTok:
Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input: {"keywords": ["#[brandname]", "#[brandname]review"], "maxItems": 200}REST API fallback — run each sequentially:
bash
# 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"}'Step 2: Sentiment Classification
Use the same lexical model for all platforms (positive/negative/neutral indicators from analyzing-twitter-sentiment-for-topic skill). Weight by platform-specific engagement:
- Twitter:
likeCount + replyCount * 3 - Reddit:
upvotes + commentCount * 2 - TikTok:
playCount / 1000 + diggCount
Step 3: Brand Health Score
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 0-100Score interpretation: 0–40 = Crisis, 40–55 = Concerning, 55–70 = Neutral, 70–85 = Positive, 85–100 = Strong.
Step 4: Edge Cases
- Brand name is a common word (e.g. "Apple"): Add qualifier ("Apple iPhone", "Apple Inc") to search to reduce noise; report disambiguation rate
- One platform dominates volume (e.g. TikTok has 10× Twitter posts): Weight by volume in the composite score
- Rapid sentiment shift (score changes > 20 points): Flag as
ALERT— may indicate PR crisis or viral positive moment - Reddit returns no results: Brand may not be discussed there; set
reddit_weight = 0and redistribute to other platforms
Output Format
# 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]"Troubleshooting
Brand 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):
bash
# 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 jsonAPIFY_TOKENmust be set in environment or.envfile.
