# Airport traveler sentiment analysis on X (Twitter)

**Use case:** 

Score traveler mood at major airports per post, with intensity and sarcasm, to track security lines, delays and lounges.

## Input

```json
{
  "searchTerms": [
    "(airport OR TSA OR \"security line\") (delay OR waiting OR lounge) lang:en -filter:retweets"
  ],
  "texts": [
    "We shipped dark mode today. Try it and tell us what breaks."
  ],
  "maxItems": 100000,
  "analysis": {
    "targets": [],
    "context": "Judge the attitude the author expresses, not the topic."
  },
  "monitor": {
    "maxBaselineRows": 100000
  },
  "twitterContent": "web scraping OR #datascience",
  "twitterHandles": [
    "elonmusk",
    "@nasa"
  ],
  "tweetIds": [
    "1846987139428634858"
  ],
  "listIds": [
    "1748648376080666720"
  ],
  "mode": "legacy",
  "queryType": "Latest",
  "lang": "en",
  "filter:has_engagement": false,
  "include:nativeretweets": false,
  "filter:twimg": false,
  "filter:native_video": false,
  "filter:vine": false,
  "filter:consumer_video": false,
  "filter:pro_video": false,
  "filter:spaces": false,
  "filter:mentions": false,
  "filter:hashtags": false,
  "includeSearchTerms": true,
  "respectProfileSubpages": false,
  "min_faves": 0,
  "-min_faves": 0,
  "min_retweets": 0,
  "-min_retweets": 0,
  "min_replies": 0,
  "-min_replies": 0,
  "filter:blue_verified": false,
  "filter:nativeretweets": false,
  "filter:replies": false,
  "filter:quote": false,
  "filter:media": false,
  "filter:images": false,
  "filter:videos": false,
  "filter:links": false,
  "filter:news": false,
  "filter:safe": false,
  "within": "10km",
  "geocode": "37.7749,-122.4194,10km",
  "includeRaw": false,
  "includeArticles": false,
  "includeUnavailableFields": false,
  "includeOriginalTweet": false
}
```

## Output

```json
{
  "tweet": {
    "label": "Original tweet",
    "format": "object"
  },
  "analysis": {
    "label": "Analysis",
    "format": "object"
  },
  "monitor": {
    "label": "Change since baseline",
    "format": "object"
  }
}
```

## About this Actor

This example demonstrates how to use [X Tweet Sentiment Analysis with AI | $0.0003/Tweet](https://apify.com/xquik/x-tweet-sentiment-analysis.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/xquik/x-tweet-sentiment-analysis.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/xquik/x-tweet-sentiment-analysis.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).