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monitoring-brand-mentions-on-twitter

Name: monitoring-brand-mentions-on-twitter
Author: apidojo-io

Monitors and aggregates brand mentions on Twitter/X using apidojo's Tweet and Search scrapers on Apify. Triggers when the user asks to: track mentions of a brand on Twitter, find what people are saying about a company on X, monitor brand sentiment on Twitter, set up brand mention tracking, find customer complaints or praise about a product on Twitter, analyze brand reputation based on tweets, or measure share of voice on X compared to competitors. Returns tweet text, author, engagement metrics, sentiment signals, and timestamps per mention. Ideal for brand managers, PR teams, community managers, and reputation analysts.

What it does

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.

The skill document

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_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: 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 report

Step 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 json

APIFY_TOKEN must be set in environment or .env file.

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] tweets

Troubleshooting

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.

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