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npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-brand-mentions-on-twitter시작 전 확인
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Monitoring Brand Mentions on Twitter
Collects all public tweets mentioning a brand, product, or keyword on Twitter/X within a date range. Groups by sentiment, surfaces top complaints and praise, and provides engagement totals.
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: Define brand terms and date range
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Retrieve dataset
- [ ] Step 4: Classify sentiment (positive/negative/neutral)
- [ ] Step 5: Deliver structured reportStep 1: Clarify Parameters
Ask the user for:
- Brand terms — brand name, handle, product name, hashtag, and common misspellings. Build a list.
Example: ["@Nike", "Nike", "#Nike", "Nike shoes"]
- Date range — e.g., "last 7 days" or specific dates
- Exclude retweets? (default: yes — filters noise)
- Min engagement (optional — e.g., tweets with ≥10 likes only)
- Language (default: all)
Step 2: Run tweet-scraper
Run once per major search term to maximize coverage. Combine results after.
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 jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["[BRAND_TERM]"],
"maxItems": 500,
"includeReplies": true,
"tweetLanguage": "en",
"since": "[YYYY-MM-DD]",
"until": "[YYYY-MM-DD]"
}If Apify MCP is not available:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"searchTerms": ["[BRAND_TERM]"],
"maxItems": 500,
"includeReplies": true,
"since": "[YYYY-MM-DD]",
"until": "[YYYY-MM-DD]"
}'Run for each brand term in the list. Wait for SUCCEEDED, collect all results.
Step 3: Fetch and Merge Results
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"Merge datasets from all runs. Deduplicate by tweet id. Result: unified list of all mentions.
Step 4: Classify Sentiment
For each tweet's text field, apply a simple classification pass:
Positive signals: words like "love", "great", "amazing", "best", "recommend", "thank", "perfect" Negative signals: words like "hate", "awful", "broken", "scam", "worst", "never again", "disappointed", "avoid" Neutral: everything else (announcements, news, questions)
Group tweets into three buckets: Positive, Negative, Neutral.
Identify top 5 most-engaged negative tweets (these need the fastest response). Identify top 5 most-engaged positive tweets (retweet candidates / testimonial material).
Step 5: Format Report
Use the output template below.
Output Format
# Brand Mention Report: [BRAND]
Period: [START_DATE] – [END_DATE] | Total mentions: [N] | Analyzed: [DATE]
## Sentiment Summary
| Sentiment | Count | % of Total | Avg Engagement |
|-----------|-------|------------|----------------|
| Positive | [N] | [X%] | [likes+RT avg] |
| Negative | [N] | [X%] | [likes+RT avg] |
| Neutral | [N] | [X%] | [likes+RT avg] |
## 🔴 Top Negative Mentions (Action Required)
1. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
2. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
3. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
## 🟢 Top Positive Mentions (Amplify These)
1. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
2. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
3. @[handle] ([likes] likes): "[tweet text excerpt]" → [url]
## Volume Over Time
[Day 1]: [N] mentions | [Day 2]: [N] mentions | [Day 3]: [N] mentions...
## Key Themes in Negative Mentions
- [Theme 1]: [N] tweets (e.g., "shipping delays")
- [Theme 2]: [N] tweets (e.g., "customer service")
## Key Themes in Positive Mentions
- [Theme 1]: [N] tweets
- [Theme 2]: [N] tweetsTroubleshooting
Too many results for popular brands: Increase minLikes filter to 5 or 10 to focus on influential mentions. Missing mentions: Twitter search API has ~7-10 day lookback limit for free tier. For historical data, reduce date range. Sentiment misclassification: Sarcasm is hard to catch with keyword matching — flag high-engagement tweets for manual review.
