X Tweet Sentiment Analysis with AI | $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 Tweet Sentiment Analysis adds attitude, intensity & sarcasm to every tweet. 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.

Measure the attitude behind X (Twitter) posts & keep the original tweet data. X Tweet Sentiment Analysis with AI collects matching tweets, then adds an AI-powered sentiment category, an intensity level & a sarcasm probability to every post. Track reactions to a launch, a campaign, an episode or a public figure, & separate loud reactions from passing mentions.

• Sentiment per post, not an aggregate score you cannot audit.
• Intensity separates emphatic posts from mild ones.
• Sarcasm probability flags posts whose literal wording contradicts the attitude.
• Complete source records with every field the tweet exposes.

How to analyze tweet sentiment

1. Add search terms, profile handles, tweet URLs or tweet IDs.
2. Set maxItems & the extraction filters your task needs.
3. Leave analysis.targets empty to judge each post on its own subject, or add names & aliases to focus the attitude on a brand, product or person.
4. Run the Actor & open the dataset.

{
  "searchTerms": ["\"season finale\" lang:en"],
  "maxItems": 200,
  "analysis": { "context": "Reactions to the show, not spoilers." }
}

What the Actor answers

Question	Answer
Sentiment	Positive, negative, mixed, neutral or unclear
Intensity	0 passing mention, 1 clear attitude, 2 emphatic wording
Sarcasm	Probability that the literal wording contradicts the attitude

When you supply targets, sentiment judges the attitude toward them & uses the quote or reply context you supply. Without targets it judges the main subject of the post.

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 filtered-out & duplicate rows are never analyzed or charged. Failed & skipped analyses & diagnostic rows have no result charge. Apify bills platform usage separately. The Pricing tab shows it.

Input & output examples

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

{
  "tweet": { "id": "2100493544842494265", "text": "…", "likeCount": 12 },
  "analysis": {
    "status": "succeeded",
    "answers": [
      {
        "questionId": "sentiment",
        "type": "choice",
        "value": "positive",
        "confidence": 0.91
      },
      {
        "questionId": "intensity",
        "type": "score",
        "value": 2,
        "confidence": 0.8
      },
      { "questionId": "sarcasm", "type": "probability", "probability": 0.04 }
    ]
  }
}

Each result contains tweet & analysis. Answers include types, question versions & available probabilities. 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. The sentiment split shows how many tweets fall into each attitude. engagementShares shows the same split with every tweet weighted by its likes, retweets, replies & quotes. top lists the three most engaged tweets per attitude. The summary rounds numbers to 4 decimals. An empty run reports zero counts & null means. Every row lists sourceDomains, the hostnames it links to, & cashtags such as $NVDA found in its text. With monitor.baselineDatasetId set, the summary's monitor block counts comparison statuses & lists up to 50 changed rows.

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.

Compare with an earlier run

Pass monitor.baselineDatasetId, the dataset ID of a completed earlier run with the same analysis settings. Every row then gains a monitor object. Its status is first_run without a baseline, new_to_baseline for tweets the earlier run did not have, & unchanged or changed for tweets it had. changes lists each sentiment, intensity level or sarcasm decision that moved from previous to current. Decisions compare by category, rounded score level, or yes/no 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-tie jitter between runs stays unchanged. Baselines above maxBaselineRows (default 100,000) or from different settings stop the run before collection with a diagnostic row.

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.

• Sentiment of season finale reactions
• Sentiment of iPhone launch posts
• Sentiment of the Super Bowl halftime show
• Sentiment toward a new electric car model
• Sentiment of Marvel movie audiences
• Sentiment of Taylor Swift album reactions
• Sentiment of a video game launch
• Sentiment about remote work
• Sentiment of airline passengers
• Sentiment of college football fans
• Sentiment about interest rate decisions
• Sentiment toward electric scooters

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 (Twitter) Brand Monitoring with AI Analysis: Tracks brand mentions with AI relevance, sentiment & customer-experience answers & compares runs. Use it when you watch a brand over time. 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: 1-8 choice, score or probability questions with 2-255 categories or at least 2 ordered levels.

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.