Text Analysis API

The API detects over 15 distinct pattern types including email addresses, phone numbers (domestic and international formats), URLs, credit card numbers with Luhn validation, social security numbers, dates in multiple formats, IPv4/IPv6 addresses, and postal codes. Beyond regex-based pattern matching, the API uses transformer-based NLP models for named entity recognition (NER), identifying people, organizations, locations, monetary values, and temporal expressions. Each detected pattern includes character offsets, confidence scores, and contextual metadata, enabling precise extraction from unstructured text for compliance scanning, data loss prevention, and content enrichment workflows.

The API supports direct text input via JSON payloads as well as file uploads in PDF, DOCX, TXT, HTML, and RTF formats. You can also analyze content directly from URLs, where the API fetches the page, strips boilerplate navigation and ads using readability algorithms, and processes the main content body. For PDF documents, the API handles both digitally-created PDFs and scanned documents through integrated OCR. DOCX processing preserves structural elements like headings, lists, and tables. The maximum file size is 10MB, and the API automatically detects character encoding for TXT files, supporting UTF-8, Latin-1, and other common encodings.

The sentiment analysis engine uses a fine-tuned transformer model trained on millions of labeled text samples across multiple domains including product reviews, social media posts, news articles, and customer support transcripts. It returns a normalized sentiment score between -1.0 (strongly negative) and 1.0 (strongly positive), along with a categorical label (positive, negative, neutral, or mixed). The API also provides aspect-based sentiment analysis, which identifies specific topics or entities within the text and assigns individual sentiment scores to each. For example, a restaurant review mentioning both food and service would receive separate sentiment scores for each aspect, enabling granular opinion mining.

The Text Analysis API supports over 50 languages for language detection with confidence scoring, and provides full NLP analysis (sentiment, entities, topics, keywords) for 12 primary languages: English, Spanish, French, German, Portuguese, Italian, Dutch, Russian, Chinese (Simplified and Traditional), Japanese, and Korean. Language detection works on text as short as 20 characters and can identify mixed-language documents, reporting the percentage breakdown of each detected language. For non-primary languages, the API still provides pattern detection, language identification, and basic entity extraction. The sentiment model supports cross-lingual transfer learning, delivering reliable results even for languages outside the primary set.

Yes, the API is designed for efficient batch analysis within a single request. You pass an array of analysis types in the request body, such as ["patterns", "entities", "sentiment", "language", "topics", "keywords", "summary", "moderation"]. Each analysis module runs in parallel on our infrastructure, so requesting multiple types adds minimal latency compared to a single analysis. The response includes a dedicated section for each requested type. You can also define custom analysis rules using the "custom" type, which accepts regex patterns, dictionaries, or classification labels. This approach eliminates the need for multiple API calls and reduces overall processing time by up to 80% compared to sequential single-type requests.

The content moderation module evaluates text against multiple safety categories including hate speech, harassment, violence, sexual content, self-harm, and spam. Each category receives an individual risk score from 0.0 to 1.0, along with an overall safety rating and a recommended action (allow, flag, or block). The model is trained on diverse datasets and regularly updated to address emerging patterns of harmful content. It handles obfuscated text, intentional misspellings, and coded language that simple keyword filters would miss. The moderation engine also provides explanation snippets that highlight the specific passages triggering each flag, making it suitable for human-in-the-loop review workflows. Configurable threshold settings allow you to adjust sensitivity levels for different deployment contexts.