# Jev Bulk Classifier — Labels, Ratings, Yes/No (`seemuapps/bulk-text-classifier`) Actor Classify, score and flag any list of texts — tickets, reviews, leads, comments — against your own labels, with one typed answer and a confidence per row. - **URL**: https://apify.com/seemuapps/bulk-text-classifier.md - **Developed by:** [Seemu Scraping](https://apify.com/seemuapps) (community) - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 evaluated texts This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. 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 examples already wired to this Actor's own input schema, see the [API](#api) section below. 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`). # README ## 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 ```json [ { "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: ```json { "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 `_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** # Actor input Schema ## `texts` (type: `array`): The texts to evaluate — one per line. Leave empty if you are reading from an existing dataset instead. ## `datasetId` (type: `string`): Evaluate the records of an existing dataset instead of the texts above — paste the dataset ID from any previous run, e.g. the output of a scraper. ## `fields` (type: `array`): When reading from a dataset, only send these fields to the model, e.g. 'text' and 'title'. Leave empty to send the whole record. ## `questions` (type: `array`): The questions to ask about every text. Each question has a 'key' (the output column name), a 'type' ('choice' picks one option, 'score' rates against ordered levels, 'noul' answers yes/no), 'instructions', and 'criteria' (options for choice, ordered levels for score). ## `maxItems` (type: `integer`): Maximum number of texts to evaluate in this run. Set 0 for no limit. ## `concurrency` (type: `integer`): How many texts to evaluate in parallel. Higher is faster; lower it if you hit rate limits. ## Actor input object example ```json { "texts": [ "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.", "Just wanted to say the new dashboard looks great. No issues here!", "How much does the Team plan cost if we add 12 seats?" ], "questions": [ { "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" } ], "maxItems": 100, "concurrency": 5 } ``` # Actor output Schema ## `results` (type: `string`): One text per record. Fields: itemIndex, text, source (when reading from a dataset), and one column per question key — plus \_confidence and \_probabilities for choice/score questions, \_label for score questions, and \_probability for yes/no questions. A record gets an 'error' field if that text could not be evaluated. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "texts": [ "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.", "Just wanted to say the new dashboard looks great. No issues here!", "How much does the Team plan cost if we add 12 seats?" ], "questions": [ { "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" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/bulk-text-classifier").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "texts": [ "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.", "Just wanted to say the new dashboard looks great. No issues here!", "How much does the Team plan cost if we add 12 seats?", ], "questions": [ { "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", }, ], } # Run the Actor and wait for it to finish run = client.actor("seemuapps/bulk-text-classifier").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "texts": [ "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.", "Just wanted to say the new dashboard looks great. No issues here!", "How much does the Team plan cost if we add 12 seats?" ], "questions": [ { "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" } ] }' | apify call seemuapps/bulk-text-classifier --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,seemuapps/bulk-text-classifier" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/fqg4aQxSQObBFyvBd/builds/e0z2DFggDeoq9RbB1/openapi.json