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_TOKEN environment 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 report

Step 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:

# 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 0-100

Score 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 = 0 and 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):

# 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

APIFY_TOKEN must be set in environment or .env file.