Text Analyser API: Automate Content Analysis at Scale

Text Analyser API: Automate Content Analysis at Scale

What it does

• Parse raw text and return structured outputs (tokens, sentences, paragraphs).
• Analyze readability, grammar issues, style, sentiment, and tone.
• Extract entities (names, places, dates), keywords, key phrases, and summaries.
• Classify content by topic, intent, or custom labels.
• Score texts with metrics (readability grade, sentiment score, confidence).

Typical endpoints

• /analyze — combined analysis (readability, sentiment, keywords).
• /entities — named-entity recognition (NER).
• /summarize — short/long summaries.
• /sentiment — polarity and intensity.
• /keywords — ranked keywords and key phrases.
• /classify — topic or custom-model classification.
• /bulk — batch-processing for many documents.

Input & output formats

• Accepts plain text, HTML, or JSON payloads; supports multipart upload for files.
• Returns JSON with standardized fields: id, text, tokens, sentences, entities[], keywords[], sentiment{score,label}, readability{score,grade}, summary, confidence.

Scalability & performance

• Supports bulk endpoints and async jobs for large datasets.
• Pagination and streaming for long results.
• Rate limits, concurrency controls, and configurable batch sizes to optimize throughput.
• Caching for repeat requests and incremental analysis for edited documents.

Security & compliance

• TLS for transport; API keys or OAuth for auth.
• Data retention configurable; support for on-prem or VPC deployments for sensitive data.
• Common compliance: SOC2, GDPR-readiness, and configurable PII redaction.

Integration patterns

• Real-time: analyze user input in web apps (autocomplete, live feedback).
• Batch: ingest content stores (CMS, data lakes) for periodic analysis.
• Pipeline: integrate with ETL, search indexing, or moderation systems.
• Event-driven: trigger analysis on new content via webhooks or message queues.

Pricing & limits (typical)

• Tiered pricing: free tier with low monthly requests, pay-as-you-go per 1K requests or per token/word processed, enterprise contracts for high volume.
• Common limits: requests/sec, max document size, monthly quota; higher tiers increase limits.

Example usage (pseudo-API request)

Code
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POST /analyze Authorization: Bearer API_KEY Content-Type: application/json{   “id”: “doc-123”,   “text”: “Your text to analyze…”,   “features”: [“readability”,“sentiment”,“entities”,“keywords”] } 

Best practices

• Pre-clean HTML to remove boilerplate before analysis.
• Use async/bulk for large corpora.
• Cache results for unchanged documents.
• Combine NER + custom entity lists for domain-specific needs.
• Monitor model drift and retrain custom classifiers periodically.