Imported from apidojo-io/apidojo-skills (skills/intent/tracking-sports-team-fan-sentiment-twitter/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill tracking-sports-team-fan-sentiment-twitter. Copyright stays with the author (Apache-2.0).

Tracking Sports Team Fan Sentiment Twitter

Executes tracking sports team fan sentiment twitter using apidojo scrapers. Part of the apidojo intelligence skills library.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

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

Step 2: Run the Actor

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": ["[TEAM_NAME]", "#[TeamHashtag]", "[TEAM_NAME] game"],
  "maxItems": 100
}

REST API fallback:

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}'

Wait for SUCCEEDED. Fetch dataset:

curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"

Step 3: Classify Results

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)

Step 4: Score Each Result

score = fan_sentiment_score = (positive_count - negative_count) / total_count  # range -1 to +1

Step 5: Edge Cases

• Sports sentiment is strongly event-driven (game results) — always note the team's recent game result as context for any sentiment measurement

Additional fallbacks:

• < 20 results: Broaden search terms; remove secondary filters
• No results: Verify the search terms are correct; try alternate phrasings
• Data quality issues: Remove entries with missing key fields; note count in output

Output Format

# 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]

Troubleshooting

Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.