Tokenise
spaCy small English model parses the text into tokens, sentences, and entity spans.
Live · NLP & Conversational AI
Sentiment + entity extraction
Paste any prose. VADER returns a sentiment score with positive / negative / neutral breakdown, and spaCy's small English model extracts named entities (people, organisations, locations, dates, money, percentages). Pure CPU, response in under 100 ms for typical paragraphs.
How it works
Sentiment
VADER applies a tuned lexicon to score positivity / negativity / intensity. Tuned for social-style and informal text.
Entity recognition
spaCy NER labels spans with PERSON, ORG, GPE, DATE, MONEY, PERCENT, and a dozen more.
Statistics
Word count, sentence count, and character count are returned alongside.
Production swap
For higher accuracy plug in a transformer model (RoBERTa for sentiment, fine-tuned BERT for NER) — same response shape, same UI.
Use cases
Ticket triage, review aggregation, contact-centre coaching, document classification, compliance checks.
Want this on your real text streams?
We build NLP pipelines for support tickets, customer reviews, contact-centre transcripts, and document libraries — calibrated to your vocabulary and tied to the KPIs your business reports on.
Ready to start
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