Jev Bulk Classifier — Labels, Ratings, Yes/No

Turn a list of texts into structured columns. Give it your own labels — support ticket queues, lead grades, sentiment, spam, intent — and every text comes back with one typed answer per question, plus a confidence score you can filter on.

No prompt engineering, no JSON parsing, no free-form text to clean up. You define the options; the answer is always one of them.

What you get

For every text, one dataset row with:

• Choice questions — the selected label (department: "billing"), a confidence score, and the probability of every option
• Score questions — a numeric rating against your own ordered levels (frustration: 1.68), the nearest level's label, confidence, and per-level probabilities
• Yes/no questions — a plain true/false plus the underlying probability, so you can set your own threshold
• The original text (and the full source record when you read from a dataset), so results stay joinable
• Export to JSON, CSV, or Google Sheets directly from the Apify console

Ask up to 10 questions at once — they are answered in a single pass per text, so adding questions costs you nothing extra in runtime.

Use cases

• Support ticket triage — route each message to the right team and flag the urgent ones
• Review and comment analysis — sentiment, topic, and "is this a complaint?" across thousands of reviews at once
• Lead qualification — score scraped profiles or form submissions against your own ICP criteria
• Content moderation and spam filtering — flag off-topic, promotional, or abusive text with a tunable confidence threshold
• Cleaning up scraper output — chain it onto any other actor's dataset to label or filter the rows before they reach your CRM or warehouse

How to use

1. Paste your texts into Texts, one per line — or put a Dataset ID from a previous run to label an existing dataset in place
2. When reading from a dataset, list the Fields to evaluate (e.g. title, body) so only the relevant text is sent
3. Define your Questions. Each one needs:
   • key — the column name in the output
   • type — choice (pick one option), score (rate against ordered levels), or noul (yes/no)
   • instructions — what you want decided
   • criteria — the options for choice, the ordered levels for score; optional for noul
4. Set Max Items (default 100; set 0 for no limit) and Concurrency
5. Run the actor — results appear in the Dataset tab

Question types

[
  {
    "key": "department",
    "type": "choice",
    "instructions": "Which team should handle this message?",
    "criteria": {
      "billing": "Payment or subscription issues",
      "technical": "Bugs or integration problems",
      "sales": "Pricing or account questions"
    }
  },
  {
    "key": "frustration",
    "type": "score",
    "instructions": "How frustrated the customer appears",
    "criteria": [
      "Calm, just stating facts",
      "Frustrated but civil",
      "Very angry, strong language"
    ]
  },
  {
    "key": "is_urgent",
    "type": "noul",
    "instructions": "The message conveys urgency or time-sensitivity"
  }
]

A choice question also accepts a plain list of labels (["positive", "neutral", "negative"]) when the labels speak for themselves.

Output format

Each dataset record:

{
  "itemIndex": 0,
  "text": "Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP.",
  "department": "technical",
  "department_confidence": 0.8,
  "department_probabilities": { "technical": 0.87, "billing": 0.13, "sales": 0 },
  "frustration": 1,
  "frustration_label": "Frustrated but civil",
  "frustration_confidence": 1,
  "frustration_probabilities": { "0": 0, "1": 1, "2": 0 },
  "is_urgent": true,
  "is_urgent_probability": 1
}

If a text cannot be evaluated, the row is still written with an error field so nothing silently disappears — the rest of the run continues.

Notes

• Each text is truncated to 8,000 characters before it is evaluated
• Confidence is separate from probability: use <key>_confidence to decide whether to act automatically or send a row to a human
• Texts longer than a few paragraphs work best when you narrow them down with Fields to evaluate