About Sentiment Analyzer
Sentiment Analyzer reads text and tells you whether its tone is positive, neutral or negative, with a confidence score for each. Analyze a single message, or paste hundreds of reviews and see the overall mood at a glance.
How it works
The tool runs a RoBERTa language model from Cardiff NLP. It was pre-trained on about 124 million social media posts and fine-tuned to classify sentiment. Because it learned from real, informal writing, it understands slang, emoji, sarcasm cues and short messages better than older word-list approaches that just count "good" and "bad" words.
Unlike keyword methods, the model reads the whole sentence in context. "Not bad at all" comes out positive, and "I wanted to love this" usually comes out negative. Each line gets three probabilities that add up to 100%, and the highest one becomes the label.
What you can use it for
- Customer reviews: find unhappy customers among hundreds of app store, Amazon or Google reviews.
- Survey responses: score open-ended feedback without reading every answer first.
- Social media monitoring: check how people react to a launch or campaign.
- Support tickets: prioritize the most negative messages.
- Writing: check that an email or announcement reads the way you intend.
Tips for the best results
- In Batch mode, put one review per line. Each line is scored separately.
- The model is tuned for short, informal English. Long formal documents are better split into paragraphs.
- Look at the scores, not just the labels. A 51% positive result is a mixed message.
- Export to CSV and sort by the negative score in your spreadsheet to build a follow-up list.
Safe for real customer data: the model runs entirely in your browser, so names, emails and order details in your reviews are never sent to a third-party API.