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Tracks sports team fan sentiment on Twitter using apidojo's Tweet scraper. Triggers when the user asks to: track fan sentiment about a sports team on Twitter, monitor Twitter reactions to sports team news, analyze fan mood after a game result on Twitter, measure public sentiment around a sports team, monitor Twitter buzz around a sports event, analyze fan reactions to player trades or news, or build a sentiment tracker for a sports team's social media presence. Returns sentiment distribution, volume trends, top fan reactions, topic themes, and event-triggered spikes. Ideal for sports marketing teams, brand sponsors, sports analytics firms, and sports media companies.
npx skill4agent add apidojo-io/apidojo-skills tracking-sports-team-fan-sentiment-twitterAPIFY_TOKEN| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
| array | ✅ | | Twitter advanced search queries (e.g. |
| string | Optional | | Sort order: |
| string | Optional | — | ISO 639-1 language code (e.g. |
| number | Optional | Unlimited | Maximum tweets to return |
| boolean | Optional | | Only tweets from verified users |
| boolean | Optional | | Only Twitter Blue subscribers |
| boolean | Optional | | Only tweets with images |
| boolean | Optional | | Only tweets with videos |
| boolean | Optional | | Only quote tweets |
| string | Optional | — | Filter to a specific author handle |
| string | Optional | — | Tweets replying to a specific handle |
| string | Optional | — | Tweets mentioning a specific handle |
| string | Optional | — | Tweets near a location |
| string | Optional | — | Radius around geotaggedNear |
| string | Optional | — | Lat/lng + radius string |
| string | Optional | — | Tweets tagged with a place |
| number | Optional | — | Minimum retweet count |
| number | Optional | — | Minimum like count |
| number | Optional | — | Minimum reply count |
| string | Optional | — | Tweets after this date (YYYY-MM-DD) |
| string | Optional | — | Tweets before this date (YYYY-MM-DD) |
| boolean | Optional | | Add the matched search term to each tweet |
| string | Optional | — | JavaScript function to transform each output object |
Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver output# 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 jsonmust be set in environment orAPIFY_TOKENfile..env
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["[TEAM_NAME]", "#[TeamHashtag]", "[TEAM_NAME] game"],
"maxItems": 100
}curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["[TEAM_NAME]", "#[TeamHashtag]", "[TEAM_NAME] game"], "maxItems": 100}'SUCCEEDEDcurl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"classification: WIN_BOOST (post-win; sentiment spike > +40%) | LOSS_DROP (post-loss; sentiment drop < -30%) | CONTROVERSY (polarized; > 30% both positive and negative) | BASELINE (normal day)score = fan_sentiment_score = (positive_count - negative_count) / total_count # range -1 to +1# Tracking Sports Team Fan Sentiment Twitter
Results: [N] | Date: [DATE]
| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |
## Summary
Top result: [description]
Key finding: [insight]