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Classify and tag customer reviews at scale
Runs every review through the same rubric and returns sentiment, a topic tag, an urgency flag and a one line summary as separate columns. Temperature is held low so the labels stay consistent across the batch, which is what makes the output usable in a spreadsheet or a dashboard rather than only readable.
Bulk LLM Runner GPT, Claude, Perplexity, Kimi (No API Key)fayoussef/bulk-llm-runner
Prompt
Model
Response
Tools used
+3 fieldsTextNumberBooleanListObject
Input
Prompts(required):Review: 'Arrived two days late and the box was crushed, but the product itself works fine.' Return sentiment (positive, neutral, negative), topic (shipping, quality, price, support, other), urgency (low, medium, high) and a one line summary.+3
System prompt (optional):You are a precise review classifier. Use only the label values offered. Return the keys sentiment, topic, urgency and summary. Do not add commentary.
Model(required):anthropic/claude-haiku-4.5
Response format:json_object
Temperature:10
Output fields
Prompt
Model
Response
Tools used
Cost (USD)
Tokens
Error
Sign up on Apify01
Create your Apify account to access the Bulk LLM Runner GPT, Claude, Perplexity, Kimi (No API Key).
Start the run02
The Actor will start running based on the input automatically.
Receive the output03
Monitor the progress in real-time. You will be notified as soon as your dataset is complete and ready for review.
Integrate into your workflow04
The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow.
