Sentiment Analyzer

Classify text sentiment as positive or negative using Workers AI and DistilBERT. Returns a label and confidence score for any short text input.

Features

• Classify text as positive or negative with confidence scores
• Workers AI DistilBERT model at the edge
• Single GET endpoint with a text query parameter
• No external ML API keys required

API Reference

GET /sentiment

Analyzes the sentiment of the provided text.

Example Request

curl "https://your-worker.workers.dev/sentiment?text=This%20pizza%20is%20great!"

Success Response

text string

The original input text

label string

Sentiment label (e.g. POSITIVE or NEGATIVE)

score number

Confidence score for the predicted label (0–1)

{
  "text": "This pizza is great!",
  "label": "POSITIVE",
  "score": 0.9998
}

Error Response

{
  "error": "Missing or invalid query parameter: text",
  "code": "INVALID_TEXT"
}

Error Codes

• 400 - Missing or invalid text (INVALID_TEXT)
• 502 - Workers AI model run failed (AI_ERROR)

Use Cases

• Score customer feedback and reviews at the edge
• Filter or route messages by sentiment in real time
• Learn Workers AI classification models in a stateless Worker
• Build moderation or analytics pipelines without external ML APIs

Limitations

• Workers AI is subject to usage limits by plan
• Input text length is capped
• Accuracy varies on very short, sarcastic, or multilingual text

Deployment

curl "https://your-worker.workers.dev/sentiment?text=This%20pizza%20is%20great!"

Local Development

cd apps/experiments/sentiment-analyzer
npm install
npm run dev

Test locally:

curl "http://localhost:8787/sentiment?text=This%20pizza%20is%20great!"

Cloudflare Features Used

• Workers - Edge compute runtime
• Workers AI - @cf/huggingface/distilbert-sst-2-int8 sentiment classification model