tracking-sports-team-fan-sentiment-twitter — Claude Code skill

---
name: tracking-sports-team-fan-sentiment-twitter
description: >
  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.
license: Apache-2.0
metadata:
  author: apidojo
  version: "1.0"
  apify-actors: apidojo/tweet-scraper, apidojo/tweet-scraper
---

# 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

| 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):**
```bash
# 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:**
```bash
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:
```bash
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.