X (Twitter) Brand Monitoring with AI Analysis | $0.0003/Tweet

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Xquik is the world's fastest & cheapest X (Twitter) scraper service with the most complete X data. X (Twitter) Brand Monitoring tracks your brand mentions with relevance, sentiment & customer-experience answers. Every other Apify Actor charges before filtering or deduplicating. Xquik charges only for delivered, unique, filter-matching results. AI costs are included in the per-tweet price. You pay no AI provider, buy no tokens & bring no key.

Monitor brand mentions on X (Twitter) & track sentiment changes between runs. X (Twitter) Brand Monitoring with AI Analysis collects every matching tweet. It answers relevance, sentiment & customer-experience questions for each post with AI. It compares those answers with an earlier dataset, so you see what changed. Every row keeps the original tweet data, so exports, reviews & follow-up analysis need no second scrape.

Watch a brand, a product line or a campaign for complaints, praise & purchase questions. Brief support & marketing teams from real posts. Keep a history of how customers talk about you from run to run.

• Every field of the source tweet. Text, author, counts, media, links, quoted & replied posts stay beside the answers.
• Typed answers. Each row has a relevance probability, a sentiment category with probabilities & a customer-experience category.
• Change tracking. Runs compare by decision, so small probability shifts do not count as changes.
• Filter-first billing. You pay only for unique, filter-matching tweets with a successful analysis.

How to monitor a brand on X

1. Add search terms (for example (Sony OR "WH-1000XM5") headphones lang:en), profile handles, tweet URLs or tweet IDs.
2. Set maxItems & the extraction filters your task needs, such as date bounds, minimum likes or reply exclusion.
3. Put your brand names & aliases under analysis.targets & describe the brand in analysis.context.
4. Run the Actor, then keep the dataset ID for your next comparison.
5. On the next run, add monitor.baselineDatasetId with that ID. Keep questions, targets, context & context limits unchanged so answers stay comparable.

{
  "searchTerms": ["(Sony OR \"WH-1000XM5\") headphones lang:en"],
  "maxItems": 100,
  "analysis": {
    "targets": [
      { "name": "Sony", "aliases": ["Sony headphones", "WH-1000XM5"] }
    ],
    "context": "Consumer headphones & customer service."
  },
  "monitor": {
    "baselineDatasetId": "YOUR_PREVIOUS_DATASET_ID",
    "maxBaselineRows": 100000
  }
}

Targets guide classification. They do not create search queries or remove irrelevant tweets automatically, so choose search terms & filters that match your research.

What the monitor answers

Question	Answer
Brand relevance	Probability that the tweet discusses your target
Sentiment	Positive, negative, mixed, neutral or unclear
Customer experience	Customer, prospect, observer or unclear

Use relevance probabilities to review ambiguous namesakes. Sentiment describes the author's expressed attitude toward the target.

How comparisons work

Comparison status	Meaning
first_run	No baseline was supplied
new_to_baseline	This tweet ID was absent from the baseline
unchanged	Every comparable decision matches
changed	At least 1 decision differs
not_comparable	Required metadata, IDs or matching settings are missing
analysis_unavailable	This tweet has no successful analysis

Answers compare by decision. A choice answer compares by its category. A score answer compares by its nearest level. A probability answer compares by its yes-or-no decision at 0.5. A decision counts as changed in three cases. The earlier category falls below 0.4 probability. A score moves at least 0.6 levels. A yes/no probability lands at least 0.1 from the threshold. Near ties between runs stay unchanged, & so do shifts that keep the same decision. Model variation between runs does not fill your report. changes lists each changed question with its previous & current decision. Changes may come from model variation, new context or edited source data. They do not prove changed facts, & an absent tweet does not prove deletion.

The baseline limit defaults to 100,000 rows. Duplicate tweet IDs, loading failures & changing dataset sizes stop comparison before collection. They never become an empty baseline.

Analyze your own text

Paste your own text in texts: drafts, replies, reviews or notes. The Actor analyzes it & fetches nothing from X.

{
  "texts": [
    "The new update is great, but sync still drops on mobile.",
    "Support fixed my issue in 10 minutes. Thank you."
  ]
}

• Each text becomes 1 row with the same analysis answers as a tweet.
• tweet.id is text:1, text:2 & so on, & tweet.type is text.
• Each analyzed text costs the same $0.0003 as an analyzed tweet.
• With texts set, the run analyzes only those texts. Run X targets separately.

Pricing

AI costs are included in the per-tweet price. You pay no AI provider, buy no tokens & bring no key.

From $0.0003 per successfully analyzed tweet, with no start fee. The price includes collection. The analysis allowance is 8 questions, 8,000 bytes per question definition & 12,000 bytes of context per tweet. Extraction filters & deduplication run before analysis, so you never pay for filtered-out or duplicate rows. Failed analyses, skipped analyses & diagnostic rows have no result charge. Apify bills platform usage for compute, storage & transfer separately at your plan's rates. The Pricing tab shows it.

Input & output examples

The input above is copy-ready. Output rows look like this (abbreviated):

{
  "tweet": { "id": "2100344867507327087", "text": "…", "likeCount": 6409 },
  "analysis": {
    "status": "succeeded",
    "answers": [
      { "questionId": "relevance", "type": "probability", "probability": 0.97 },
      {
        "questionId": "sentiment",
        "type": "choice",
        "value": "neutral",
        "confidence": 0.88
      },
      {
        "questionId": "experience",
        "type": "choice",
        "value": "observer",
        "confidence": 0.69
      }
    ]
  },
  "monitor": { "status": "unchanged", "changedQuestionIds": [], "changes": [] }
}

Each result contains tweet, analysis & monitor. Answers include types, question versions & available probabilities. Missing quote, reply, author & media context stays explicit under analysis.contextAvailability. A failed or skipped analysis keeps the collected tweet with an empty answer list & a reason. Free diagnostics in the key-value store explain invalid inputs, missing results & interrupted collection, & the run report separates collected rows, charged analyses & pending charges.

Run summary & flat answers

Each run writes an analysis-summary record to its key-value store & repeats it under results.analysisSummary in the run report. It counts analyzed, failed & skipped rows, sums engagement, and summarizes every question.

• targets reports mentions, share of voice & engagement per brand or alias.
• Each targets entry has top, its three most engaged mentions per answer category. Use it to alert on the strongest negative & positive mentions.
• Each targets entry has choices, the answer split among tweets that mention that brand.
• The sentiment block lists the three most engaged positive & negative mentions under top.
• relevance counts the mentions that are about the brand.
• monitor.changedRows lists tweets whose decisions moved since the baseline. Send them to a webhook or an alert.
• With monitor.baselineDatasetId set, the monitor block counts comparison statuses & lists up to 50 changed rows.
• Every row lists sourceDomains, the hostnames it links to.

The summary rounds numbers to 4 decimals. An empty run reports zero counts & null means.

Every result row also carries answers, a flat map from question ID to the chosen category, score, or probability. The Flat answers dataset view & CSV or Excel exports show one column per question beside the tweet, so spreadsheets need no JSON parsing. Failed & skipped rows carry an empty map.

Task examples

Choose from 50 public tasks. Each starts from a real English search with a bounded maxItems, ready-made targets & context, & the overview dataset view. Edit the search or targets before running.

• Monitor Nike brand mentions on X
• Monitor Starbucks brand mentions on X
• Monitor Tesla brand mentions on X
• Monitor Spotify brand mentions on X
• Monitor Netflix brand mentions on X
• Monitor Airbnb brand mentions on X
• Monitor Uber brand mentions on X
• Monitor Peloton brand mentions on X
• Monitor Shopify brand mentions on X
• Monitor Notion brand mentions on X
• Monitor Duolingo brand mentions on X
• Monitor Lululemon brand mentions on X

The remaining tasks cover more brands, topics & markets on the Actor page.

Related Xquik Actors

Every Xquik Actor shares the same extraction engine, filter-first billing & diagnostics. Pick the one that matches the data you need.

• X Tweet Scraper: Scrapes tweets from searches, profile timelines, Lists & tweet IDs with 50+ filters & flat exports. Use it when you need tweet data without analysis. From $0.00015 per row.
• X Profile Scraper: Scrapes profiles plus their posts, replies, media & followers from handles, IDs or URLs. Use it when you start from accounts rather than searches. From $0.00015 per row.
• X Reply Scraper: Scrapes replies, comments & whole conversations under posts with 25+ filters. Use it when you need the discussion beneath tweets. From $0.00015 per row.
• X Engagement Scraper: Scrapes replies, quotes, retweeters & threads for post URLs or IDs in bulk. Use it when you measure who engaged with posts. From $0.00015 per row.
• X Follower Scraper: Scrapes followers, following, List members, subscribers & Community members as profile rows. Use it when you need audience or member lists. From $0.00015 per profile.
• X User Search Scraper: Searches users by handle, bio & location with follower, verification, age & location filters. Use it when you build account lists from search. From $0.00015 per profile.
• X List Scraper: Scrapes List posts, members & followers from List URLs or IDs. Use it when a curated List defines your sources. From $0.00015 per row.
• X Community Scraper: Scrapes Community info, posts, searches, members & moderators. Use it when your sources are X Communities. From $0.00015 per row.
• X Trends Scraper: Scrapes real-time trends by location with rank, volume, query & WOEID. Use it when you track what is trending where. From $0.00015 per trend.
• X Article Scraper: Scrapes long-form X Articles as Markdown & text with covers, authors, dates & metrics. Use it when you need article bodies, not tweets. From $0.00015 per article.
• X Media Downloader: Extracts or stores photos, videos & GIFs from posts or profiles with MP4 & metadata options. Use it when you need the media files themselves. From $0.00015 per media row.
• X Tweet Sentiment Analysis with AI: Labels attitude, intensity & sarcasm probability for every tweet with AI. Use it when you need general sentiment on any topic. From $0.0003 per analyzed tweet.
• X (Twitter) Stock & Crypto AI Trading Signals: Labels bullish, bearish, neutral or mixed stance, content type, conviction & asset relevance with AI. Use it when you follow stocks, crypto or trading talk. From $0.0003 per analyzed tweet.
• X (Twitter) News Monitor with AI Analysis: Labels news posts by format, source attribution & topic relevance with AI. Use it when you separate reporting from commentary. From $0.0003 per analyzed tweet.
• X Tweet Classifier with AI Analysis: Answers your own category, score & yes/no questions for every tweet with AI. Use it when the preset analyses do not fit your labels. From $0.0003 per analyzed tweet.
• X Tweet Viral Score Analyzer with AI: Estimates a Viral Score from 0 to 100 & a verdict for every tweet from 8 AI trait answers. Use it when you study why tweets spread or flop. From $0.0003 per analyzed tweet.

FAQ & support

Can I use my own questions?

Yes. Custom analysis.questions replace the defaults. Send 1-8 choice, score or probability questions. Choice questions accept 2-255 categories. Scores use at least 2 ordered levels. Keep the same questions across runs you want to compare.

Why did a row come back with analysis.status of failed or skipped?

The Actor collected & delivered the tweet, but the AI analysis did not complete. analysis.reason names the cause, such as context_limit when the tweet & its context exceed maxContextBytes, or service_unavailable after retries. These rows carry no result charge. Raise maxContextBytes (up to 12,000) or rerun the affected IDs.

Does the analysis verify facts?

No. Answers describe what the post expresses & how the post frames it. Probabilities express model confidence, not truth. Review important classifications against the original tweet, which every row keeps.

Which languages work?

Extraction supports every language X serves. We validate analysis on English customer scenarios first. Other supported languages return answers with the same structure. unclear categories & probabilities show uncertainty in every language.

How do I limit cost?

Filters, deduplication & maxItems run before analysis, so the Actor analyzes & charges only unique, filter-matching tweets. Use precise search operators, date bounds & engagement floors, & start with a small maxItems to check answer quality before a large run.

Where do I get help?

Open an issue on the Actor page or contact support@xquik.com with the run ID. Free diagnostics in the key-value store explain empty, partial or interrupted runs.

Xquik is an independent third-party service. Not affiliated with X Corp. "Twitter" and "X" are trademarks of X Corp.