Our demo service uses generic models trained on real user's comments, product, service opinions. In order to get specific results that are tailored to your domain, please consider training your own sentiment model.
Please enter your text in english* for analysis or leave default one.
Don't mind the books on the list but, bro, you are not reading Brothers Karamazov in one week.
This document is:
negative (-0.64)
Magnitude: 0.85
Subjectivity: subjective
Subjectivity: subjective
Score Range
negative neutralpositive
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Train your own document |
Train your own sentiment model |
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|
 |
| Core sentences | Magnitude | Sentiment Score |
|---|---|---|
| Don't mind the books on the list but bro you are not reading Brothers Karamazov in one week. | 0.85 | -0.530 |
Auto categories [IAB QAG taxonomy]
| Category | Score |
|---|---|
| Books & Literature | 0.231 |
No user categories detected
About analysis
- Once analysis is finished, you will see the overall score for the document and extracted thematic concepts, entities, keyword phrases, auto categories..
- In longer documents, entity/theme sentiment is in general more useful.
- The better input text is formatted (properly placed commas, spaces between sentences etc.), the faster and more accurate analysis will be returned.
- Twitter mode is usually more accurate for short, unformatted contents. Includes irony, slang and abbreviation detection. In this mode additional text cleaning is performed, inluding removal of usernames (starting from @), links, numbers and special characters. Hashtags are being left for analysis.
- Magnitude is the volume of sentiment expressed regardless of sentiment polarity, it can be used to detect strength of emotions or fine-tune sentiment polarity. It ranges from 0 to ∞.
