apidojo-io 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_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 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_TOKENmust be set in environment or.envfile.
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.